what is qualitative and quantitative research methods

Focuses on testing hypotheses. Can also be used to determine general facts about a topic.

Focuses on developing an idea or hypotheses. Can also be used to gain a deeper understanding into specific topics.

Analysis is mainly done through mathematical or statistical analytics.

Analysis is more interpretive and involves summarizing and categorizing topics or themes and interpreting data.

Data is typically expressed in numbers, graphs, tables, or other numerical formats.

Data is generally expressed in words or text.

Requires a reasonably large sample size to be reliable.

Requires smaller sample sizes with only a few respondents.

Data collection is focused on closed-ended questions.

Data collection is focused on open-ended questions to extract the opinions and views on a particular subject.

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

It's Not Always One or the Other!

It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods. 

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Qualitative vs. quantitative research: A simple guide

Quantitative research deals with numbers and statistics, while qualitative research involves pulling information from experiences and stories.

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From Tesla to Tushy, every successful brand is built on a foundation of both quantitative and qualitative research. Marketers and product developers use this zero-party data to frame their advertising strategies, product positioning, and brand voice—basically, everything that goes into designing and selling a product or service.

When it comes to qualitative vs. quantitative research, both methods have their benefits and drawbacks in certain applications. We break down what you need to know before running your next round of market research. 

Qualitative vs. quantitative research: What’s the difference?

Quantitative research counts and measures numbers to find statistical patterns, while qualitative research is a deep dive into understanding people’s thoughts and experiences. They're similar in that they both aim to uncover valuable insights, but they use different tools and approaches to do so.

But don’t be fooled into thinking that one research method is better than the other—both require systematically applied research methods and analysis.

What is qualitative research and data?

Qualitative research is like the Sherlock Holmes of the research world—it seeks to uncover the hidden stories, motivations, and intricacies that numbers can't reveal. Instead of crunching data, it dives deep into people's experiences, thoughts, and feelings to help explain certain behaviors and patterns. 

In qualitative research, it's not about numbers but rather words, pictures, and observations. You'll collect rich, unstructured data via interviews, focus group discussions, or open-ended surveys. 

Say you're a marketing rep keen on understanding how people perceive your smartphone brand. 

First, you organize a series of in-depth interviews with smartphone users, asking open-ended questions about their experiences with the brand. Participants share stories about their interactions, likes, dislikes, and emotional connections with the product. You also delve into social media posts, online reviews, and forum discussions to gauge the brand's online reputation.

As you analyze this data, patterns begin to emerge. You find that users consistently describe the brand as "innovative" and "user-friendly." However, you also discover a recurring frustration with battery life and customer support. Qualitative research not only provides you with insights into how people perceive the brand but also dives into the emotional nuances behind their perceptions. Armed with this knowledge, you can fine-tune your advertising campaigns and product improvements to align with your target audience's genuine feelings and experiences.

Pros and cons of qualitative research

Qualitative research is your go-to when you want to explore the human side of data. It's like having a heart-to-heart conversation with your research subjects. Just keep in mind that, like any detective work, it comes with its own quirks and challenges.

Deep insights: It's great at uncovering the "whys" and "hows" behind human behavior, providing rich insights that quantitative data can miss.

Flexible and exploratory: Qualitative research allows for flexibility, so you can adapt your questions and approach when you face the unexpected.

Humanizing data: Unlike numbers, qualitative research humanizes data by bringing stories and personal experiences to the forefront. It's perfect for capturing human nuances and emotions.

Subjectivity: Different researchers might draw different conclusions from the same data based on their own personal feelings, experiences, or opinions, so it's crucial to stay aware of potential bias.

Resource-intensive: Qualitative research demands time and effort. Conducting interviews, transcribing, and analyzing data is a labor-intensive process, which might not suit all budgets or timelines.

Smaller samples: Your pool of participants tends to be smaller compared to quantitative research, making it challenging to generalize findings to a larger population. It's like diving deep into a few personal stories rather than looking at the bigger picture.

Can’t always be automated: Unlike quantitative research, where you can automate data collection and analysis with software, qualitative research relies heavily on human interaction and interpretation. You can, however, create a survey with open-ended questions to collect qualitative data. Better yet, try our VideoAsk feature, which allows you to ask questions via pre-recorded video and lets respondents answer in video, voice, or text format, preserving that ever-important human element that defines qualitative data. 

What is quantitative research and data?

Quantitative research is all about numbers, statistics, and cold, hard data. It’s more structured and objective and helps reduce researcher biases . It gets at the “what” of a person’s behavior by answering questions like how many, how often, and to what extent?

Let’s look at quantitative research in action. Imagine you're trying to pinpoint the target market for your new fitness app. You survey the app's users, collecting data on their age, gender, location, and fitness habits. The data reveals that 75% of your target users are ages 18-34, with a nearly even split between men and women. You also notice that users in urban areas are 20% more likely to use your app regularly than those in rural areas.

Quantitative research doesn't stop at just counting, though. It's also about analyzing data to spot trends and differences. In this case, it's clear that your core audience consists of younger adults in urban settings, and you can tailor your marketing strategies and app features to better cater to this demographic. So, if you're a number-crunching, stats-loving kind of researcher, quantitative research is your jam.

Pros and cons of quantitative research

In a nutshell, quantitative research is your go-to when you want solid, numerical answers. But remember, it won't tell you the whole story, and sometimes, life's questions are a bit too complex for a numbers-only approach. Keep these pros and cons in mind when running your next quantitative study:

Precision with numbers: Quantitative research is like a laser-guided missile for numbers. It offers precise measurements and statistical analysis, which is great when you need concrete answers.

Reproducibility: It's a cookie-cutter approach—your methods and results can be replicated by others, making it a cornerstone of scientific rigor.

Generalizability: You can often apply findings to a larger population—if it works for one group, it might work for a similar one.

Limited bias: Quantitative research can be a bias-buster. With structured surveys, standardized data collection methods, and statistical analysis, it's easier to minimize researcher bias and keep the study objective. 

Fewer resources: If you're watching your budget, quantitative research may give you more bang for your buck. It often requires fewer resources in terms of time, personnel, and money, making it a practical choice, especially for smaller-scale research projects.

Limited depth: While it's king of numbers, quantitative research can be a bit shallow in understanding. It's like knowing the “what” but not the “why.”

Context ignored: Sometimes context gets lost in a sea of numbers, and you might miss the bigger picture.

Inflexibility: If your research question isn't easily quantifiable, you might end up with results that are difficult to decipher. Not everything can be counted or measured.

Which is better: Qualitative or quantitative research?

It’s a trick question. We’re not pitting qualitative and quantitative research against each other. However, one may prove more useful than the other, depending on your research goals. 

For example, it’s best to stick with qualitative research when:

You want to explore in-depth: Choose qualitative research when you need a deep understanding of a complex phenomenon, like customer perceptions or human behavior. It's like peeling back the layers of an onion to uncover the core.

You need to generate hypotheses: Qualitative research is fantastic for generating ideas or hypotheses that you can later test with quantitative research. 

You value the human perspective: If you want to capture emotions, stories, and personal experiences, opt for qualitative research. It's your go-to when you're interested in "the why" rather than just "the what."

On the other hand, quantitative research may prove more valuable if:

You need to measure and quantify: If you're after hard numbers, like percentages, averages, or correlations, quantitative research is your go-to.

You want to generalize to a larger population: Quantitative research allows you to make statistically valid generalizations to a broader audience. If you plan to reach a wide market, this is your best bet.

You prefer structured and standardized data collection: When consistency and minimizing bias are critical, quantitative research methods like surveys and online tests provide a structured and uniform approach. 

However, you aren’t limited to just one type of research method. You can use both qualitative and quantitative data to give you the most insightful information when:

You need a comprehensive understanding: Sometimes, using both qualitative and quantitative research sequentially is the ideal approach. Start with qualitative research to explore a topic, identify key variables, and generate hypotheses. Then, use quantitative research to test those hypotheses on a larger scale, ensuring a more comprehensive understanding.

You want to validate findings: When you've conducted qualitative research and want to make sure your findings are not just anecdotal, quantitative research can validate and generalize your insights to a broader population.

You're tackling a complex problem: For multifaceted issues, using both approaches can provide a well-rounded view. Qualitative research can uncover the depth and nuances, while quantitative research can quantify the extent of the issue and help prioritize actions.

Quantitative research provides evidence and predictions. Qualitative research provides context and explanations. So which one is best for you? That depends on the questions you need answered.

Research methods

Quantitative and qualitative research methods are systematic ways of collecting data and testing hypotheses. And guess what? It’s something you already do all the time.

We constantly take in information from our surroundings to figure out how to interact with the people around us.

The same goes for market research . A company tries to learn more about their customers and the market. Why? To develop an effective marketing plan or tweak one they already have. The method you use to do this depends on the data that will answer your key questions.

Qualitative research methods

Here are some of the most common qualitative research methods:

In-depth interviews: Known as IDI in market research circles, in-depth interviews are ideal for digging into people’s attitudes and experiences. 

Case studies: In-depth analysis of a single case or a few cases are best suited for investigating unique or complex cases in depth

Focus groups: These are effective for getting several opinions in a conversational format. Participants lead the discussion, while a facilitator guides the conversation through a list of topics, questions, or projective exercises.

Participant observation: Simply engaging and observing your audience day-to-day provides a firsthand view of how people interact in real-life situations.

Historical research: Exploring historical documents and records helps you examine the past through primary and secondary sources, contributing to our understanding of historical events and trends and how they may relate to the current scenario.

Qualitative surveys: Surveys comprised of open-ended questions provide an automated way to receive qualitative data through a quantitative approach..

Ethnography: Ethnography is a broad market research approach that involves all of the methods above in order to gain a comprehensive understanding of the culture or community being studied. 

Quantitative research methods

Here are some of the most common quantitative research methods:

Surveys: Surveys conducted online, over the phone, and even in person with structured interview questionnaires are an efficient way of collecting data from a large pool of participants. 

Polls: Polls are one- or two-question surveys that are often used to gauge public opinion on an important matter (or a frivolous matter—it’s your poll). Because polls are only one or two questions, analysis is pretty much immediate.

Structured observation: This is a structured form of ethnography used to measure certain actions or behaviors, such as tracking how many boxes of cereal people pick up before choosing one to purchase.

Experiment: Market researchers conduct controlled, manipulated, or randomized experiments to understand how specific variables influence outcomes through methods like A/B testing or pilot testing.

Quizzes: Answering a few general questions to find out which Harry Potter character you are may seem like fun and games, but interactive quizzes are a great tool for gathering information while keeping your audience engaged. 

Secondary data analysis: This cost-effective research method taps into big existing datasets like government databases or company records to pull relevant data. 

Mixed research methods

Mixed research methods combine both qualitative and quantitative approaches to provide a comprehensive understanding of the question at hand. Some of the most common mixed research methods include:

User testing: You’ve heard the phrase “Show, don’t tell.” So rather than asking people to explain their experiences, why not have them show you? User testing can tell you where you thrive and fall short, so you can adjust your marketing strategy accordingly.

Help transcripts: Live chat or call transcripts can yield both qualitative and quantitative data. Reading and coding them can help you understand people’s pain points and challenges throughout your conversion funnel.

Customer reviews: Look beyond your own surveys and check sites like Yelp or Google reviews. What are people saying about you? What do they like and dislike? The things people say and how often they say it can yield robust qualitative and quantitative data.

Data analysis

Data analysis is the search for patterns in data, followed by the interpretation of that information to help explain why those patterns are there.

It’s important to keep in mind that quantitative and qualitative data aren't mutually exclusive.

Qualitative data can be translated into quantitative data. For example, you could count the number of times interviewees used a particular word to describe your product to yield quantitative data.

Similarly, quantitative methods of analysis require you to explain what the patterns mean and connect them to other parts of your business—a qualitative exercise!

Qualitative data analysis example

Qualitative data can be difficult to analyze since it’s largely made up of text, images, videos, and open-ended responses instead of numbers. Examples of qualitative data analysis include:

Thematic analysis: Identifying and categorizing recurring themes, patterns, or concepts within the data to uncover the most prevalent and significant themes in your dataset

Content analysis: Examining large amounts of text, visuals, or audio content to identify themes or patterns 

Discourse analysis: Dissecting the language used in the data to understand how individuals or groups construct meaning and social reality through their discourse

Cross-case analysis: Comparing and contrasting multiple cases to identify commonalities and differences, helping to develop broader insights

Quantitative data analysis example

Quantitative data analysis is all about crunching numbers. It can involve presenting data models such as graphs, charts, tables, probabilities, and more.

Tools like Excel, R, and Stata make it easy to track quantitative data like:

Average scores and means

The number of times a specific response is recorded

Connections or potential cause-and-effect relationships between two or more variables

The reliability and validity of results 

Get the right data with Typeform

Congrats—you’ve learned all about the differences between qualitative vs. quantitative research.

Now, the key to successful data collection is iteration.

That doesn’t mean doing the same thing again and again.

It means continually returning to your questions, methods, and data to spark new ideas and insights that'll level up your research —and your business.

Typeform makes it easy to design and automate forms that collect both quantitative and qualitative data—no extensive interviews or focus groups required. With conditional formatting and various question types, you can gather the information you need to get more customers.

About the author

Lydia is a content marketer with experience across both the B2B and B2C landscapes. Besides marketing and content, she's really into her dog Louie.

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Distinguishing quantitative & qualitative methods, word clues to identify methods.

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What’s the Difference Between Qualitative and Quantitative Methods?

From: Suter, W. N. (2012). Qualitative Data, Analysis, and Design. In  Introduction to educational research: A critical thinking approach . SAGE Publications, Inc., www.galileo.usg.edu/redirect?inst=pie1&url=https://dx.doi.org/10.4135/9781483384443

The words in this table can be used to evaluate whether an article tends more toward the quantitative or qualitative domain. Well-written article abstracts will contain words like these to succinctly characterize the article's content.

Adapted from: McMillan, J. H. (2012).  Educational research: Fundamentals for the consumer  (6th ed.). Boston, MA: Pearson.

Search SAGE Research Methods for resources about qualitative methods

Search SAGE Research Methods for resources about quantitative methods

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Quantitative and Qualitative Research

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

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Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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Qualitative versus Quantitative Research for Beginners

Definitions: Two sides of the same coin Research: it's the beating heart of progress. It fuels innovation, sheds light on unknown territories, and informs decisions. But just as a coin has two sides, so does research: meet Qualitative and Quantitative research, the two dynamic heroes of our story. Qualitative research, the explorer of our duo, seeks to understand the world from the participant's viewpoint. It delves into the depth of 'why' and 'how' a phenomenon occurs, providing insights into people's motivations, thoughts, and feelings. On the other hand, Quantitative research, our numerical navigator, quantifies the data to yield measurable, statistical insights. It asks 'how much' or 'how many', and delivers results in numbers, charts, and graphs. Both types are invaluable, both unique. And both are vital tools in the toolbox of every researcher. Comparison table: Differences between qualitative and quantitative research   Qualitative Research Quantitative Research Nature Subjective, exploratory Objective, conclusive Data Non-numerical, descriptive Numerical, statistical Goal Understanding ‘why’, ‘how’ Measuring ‘how much’, ‘how many’ Methods Interviews, observations, case studies Surveys, experiments, polls Analysis Thematic, content, discourse Statistical, mathematical Outcome Deep, rich insights Generalizable results What do qualitative and quantitative research have in common? Is quantitative research better than qualitative? One is not better than the other—the truth, as is often the case, lies somewhere in the middle. Both are powerful in their own right, and both share a common goal: to explore, understand, and contribute to our knowledge. Choosing the appropriate method depends on your research question, objectives, and resources. They are two sides of the same research coin, both offering a wealth of insights. Pros and Cons: When to use qualitative and quantitative research Qualitative and quantitative research are like two arrows in a researcher's quiver, each with its own strengths and weaknesses. Understanding these can help you choose the most appropriate method for your study. a) Advantages of Qualitative Research In-Depth Understanding: It's the Sherlock Holmes of research. Qualitative research probes deep into the matter to extract rich insights and unravel intricate details. Flexible and Adaptive: Unlike rigid survey forms, qualitative research can evolve with the study, enabling the researcher to probe emerging trends in real-time. Contextual: By considering the environment and social norms, qualitative research ensures a holistic view of the phenomena. Human-Centric: It centers on human experiences, emotions, and behaviors, making it ideal for exploratory research. b) Limitations of Qualitative Research Time and Resource Intensive: Conducting interviews or observations requires substantial time, which might be a constraint for some studies. Subjectivity: The presence of the researcher can influence the participant's responses, potentially introducing bias. Non-Generalizable: The findings are context-specific and may not be applicable to the larger population. Requires Expertise: Analyzing qualitative data needs a seasoned researcher with a keen eye for detail. c) Advantages of Quantitative Research Quantifiable: Love numbers? So does quantitative research. It provides measurable data, making it easier to identify trends and patterns. Replicable: The structured approach ensures that the study can be replicated, enhancing the validity of the findings. Generalizable: Large sample sizes allow for generalizations about the population, offering broad insights. Unbiased: The use of statistical techniques helps reduce bias, ensuring objectivity. d) Limitations of Quantitative Research Limited in Depth: While it tells you 'how many,' it doesn't explain 'why.' Less Flexible: The structured format doesn't allow for probing or adapting the study based on participant responses. Decontextualized: Quantitative research may ignore the context, potentially oversimplifying complex phenomena. Requires Large Sample Size: To ensure representativeness and reduce error, quantitative research requires larger samples, which may not always be feasible. e) How Do Qualitative and Quantitative Research Work Together? Qualitative and quantitative research aren't adversaries—they're allies. Think of them as two pieces of a puzzle, each contributing to a comprehensive picture. For instance, a researcher may start with qualitative research to explore a phenomenon, and then use quantitative research to measure the trends observed. f) How to Choose the Best Design? Choosing the right design is like picking the right tool for a job—it depends on your objectives, your questions, your available resources, and your time constraints. Remember, qualitative research is your go-to for exploring 'why' and 'how.' If you seek to understand experiences, behaviors, or the underlying reasons, qualitative is your friend. For measuring 'how much' or 'how many,' or for studying relationships between variables, turn to quantitative research. Consider a mixed-method approach if you want the best of both worlds, but be prepared—it demands time, resources, and expertise. With the right tools in hand, the world of research is your oyster. Let's march on and explore the methods used to gather data in qualitative and quantitative research. Stay tuned! Methods: How to gather data in qualitative and quantitative research Every research endeavor begins with data collection. Both qualitative and quantitative research employ distinct methodologies that speak to their respective goals and applications. a) Qualitative Methods In-Depth Interviews: Like a friendly conversation, these interviews explore a participant's perspectives, experiences, and motivations in detail. They are flexible and allow for follow-up questions. Focus Groups: Think of it as a roundtable discussion. A group of people discusses a topic, providing a variety of perspectives and interactions to analyze. Observations: Actions speak louder than words! This method involves observing participants in their natural environment, capturing behavior that might not emerge in a formal interview. Case Studies: A case study is an in-depth analysis of a single 'case'—it could be a person, a group, or a specific context. It's like the biography of a research subject. Ethnography: Walk a mile in their shoes. Ethnography involves immersing oneself in the participant's environment to understand their culture, behaviors, and interactions. b) Quantitative Methods Surveys: A classic! Surveys can collect data from a large group using pre-determined questions, making it easier to quantify and compare responses. Experiments: Cause-effect relationship, anyone? Experiments manipulate one variable to study its impact on another, offering conclusive evidence. Observational Research: Observational research in a quantitative context involves systematic collection and categorization of observed data to derive statistical insights. Secondary Data Analysis: Why reinvent the wheel? This method involves analyzing data collected by someone else. Think census data or company reports. Longitudinal Studies: Time travel, the research way! Longitudinal studies collect data over an extended period to track changes and detect trends. Next, let's delve into how we make sense of all this data—welcome to the world of data analysis! {loadmoduleid 430} Data Analysis: How to Analyze Qualitative and Quantitative Data After collecting data, the next crucial step is data analysis, where we transform raw data into meaningful insights. Both research types use different analytical approaches that complement their distinct objectives and data characteristics. a) Qualitative Data Analysis Qualitative data analysis is all about understanding the context, meaning, and patterns hidden within the data. This is often done through: Thematic Analysis: This method involves identifying and analyzing patterns (or 'themes') within the data. Researchers meticulously go through the data, annotating and grouping segments of text by theme. Narrative Analysis: Stories reveal a lot! In narrative analysis, researchers explore participants' narratives to understand their experiences and perspectives. Discourse Analysis: Beyond what is said, how it is said matters. Discourse analysis looks at the language used, considering factors like sentence structure, word choice, and conversation flow. Grounded Theory: The theory grows from the data! Grounded theory uses iterative data collection and analysis to develop theories rooted directly in the collected data. Content Analysis: In content analysis, qualitative information (like text or media) is categorized and counted to identify patterns and frequencies. b) Quantitative Data Analysis Quantitative data analysis aims to quantify relationships between variables and generalize findings. This can be achieved through: Descriptive Statistics: These give a summary of the data through measures like mean, median, mode, and standard deviation. Inferential Statistics: Want to make predictions? Inferential statistics uses sample data to make predictions about a population or test hypotheses. Regression Analysis: Regression models the relationship between a dependent variable and one (or more) independent variables. Factor Analysis: Factor analysis groups related variables together, reducing the data's dimensionality and making it more manageable. Time Series Analysis: Time matters! Time series analysis examines data points collected over time to identify trends or cycles. Validity and Reliability Validity and reliability are two critical considerations in both qualitative and quantitative research. These are the pillars that uphold the quality of research findings and conclusions. Validity relates to the accuracy and truthfulness of the research findings. It's about whether the research genuinely measures what it intends to measure. In qualitative research, this is often ensured through credibility, transferability, confirmability, and dependability. In contrast, quantitative research uses internal and external validity. Reliability refers to the consistency and repeatability of the research results. If the research were to be replicated under similar conditions, the findings should be more or less the same. In qualitative research, reliability is ensured through dependability, while in quantitative research, reliability is measured using tools like Cronbach’s Alpha. Sample Size The sample size in a research study can greatly influence the results. Qualitative research typically uses smaller sample sizes, as it is more focused on understanding concepts, thoughts, and experiences in-depth. On the other hand, quantitative research often requires larger sample sizes to ensure the findings' statistical significance. Questions The type of questions asked in qualitative and quantitative research also differ greatly. Qualitative research questions are often open-ended, exploratory, and focus on the participants' experiences and perspectives. Here are a few examples: Can you describe your experience using our mobile app? How did you feel when you first started using our product? What factors influenced your decision to purchase from our brand? Can you tell us about a time when our customer service exceeded your expectations? In what ways has our product impacted your daily routine? On the other hand, quantitative research questions are more closed-ended, looking for specific, measurable answers. Here are some examples: On a scale of 1-10, how satisfied are you with our product? How many times a week do you use our service? Would you recommend our product to a friend or colleague? (Yes/No) How much time do you spend on our website during a typical visit? Which feature of our product do you use the most? (Multiple choice) These questions demonstrate the distinct objectives and outcomes of qualitative and quantitative research. Now, let's delve into real-world examples across various sectors to see these research methods in action! Examples Each research method has its unique merits, and this is illuminated when we look at them in specific contexts. Here's how qualitative and quantitative research can be applied across various fields: a) Healthcare examplesIn healthcare, qualitative research may be used to understand patient experiences with a specific treatment, where they can share their feelings and perceptions freely. On the other hand, quantitative research can be used to measure the effectiveness of a new drug, with concrete data like patient recovery rates. b) Nursing examplesNursing research might employ qualitative research to understand the experiences of patients living with chronic illness, gathering rich, detailed narratives. Quantitative research may be used to identify patterns, like the impact of a new hygiene protocol on the rate of hospital-acquired infections. c) Psychology examplesIn psychology, qualitative research can be used for in-depth exploration of complex phenomena like stress perception. Quantitative research, on the other hand, is often used to test hypotheses, like the correlation between sleep duration and cognitive performance. d) UX examplesUser Experience (UX) researchers often use qualitative research to get rich insights about a user's experience with a product, often using techniques like user interviews. Quantitative research can provide hard numbers on usage patterns, like the percentage of users who abandon their shopping carts. e) Marketing examplesMarketing teams may use qualitative research to delve into consumer attitudes towards a brand or product. Quantitative research can be used to track measurable outcomes, like the impact of an ad campaign on sales numbers. f) Social work examplesIn social work, qualitative research can offer a detailed understanding of the experiences of individuals in a community. Quantitative research can provide data on larger-scale patterns, like the prevalence of unemployment in that community. g) Sociology examplesSociologists may use qualitative research to understand the dynamics within a particular social group, with in-depth interviews. Quantitative research can provide broader trends in society, like the correlation between education levels and income. h) Education examplesEducational researchers can use qualitative research to understand the experiences and challenges of students in a classroom setting. Quantitative research can provide measurable outcomes, like the impact of a teaching method on standardized test scores. i) Counseling examplesIn counseling, therapists may use qualitative research to understand a client's personal narrative better. Quantitative research can help in measuring the effectiveness of a specific therapeutic intervention. j) Criminal justice examplesCriminal justice researchers might use qualitative research to understand the experiences of individuals in the justice system. Quantitative research can provide hard data on crime rates or the effectiveness of a rehabilitation program. k) Law examplesIn law, qualitative research can be used to understand the experiences and perspectives of individuals involved in a legal case. Quantitative research may be used in broader legal research to identify patterns or correlations, like the relationship between certain laws and crime rates. Psychology Finally, let's turn our gaze towards psychology, a field where both qualitative and quantitative research play vital roles. Psychology, as a discipline, studies human behavior and the mind. Both qualitative and quantitative research approaches contribute to its breadth and depth. Qualitative research in psychology might involve studying a small group of individuals with a rare psychological condition, using in-depth interviews to gather rich and detailed data. It can help explore intricate phenomena such as emotions, thought processes, or experiences that are difficult to capture with numerical data. On the other hand, quantitative research in psychology might involve testing a hypothesis about the impact of screen time on attention span across a large sample size, using structured methods like surveys or experiments. This approach allows for statistical analysis, which can highlight patterns, correlations, or cause-and-effect relationships. Together, qualitative and quantitative research methods help paint a complete picture, providing both the detailed context and broad trends needed to advance psychological understanding. Conclusion In this blog post, we've taken a deep dive into the world of qualitative and quantitative research. We've seen that these two methodologies, while distinct, often complement each other to provide a well-rounded understanding of the research question at hand. We hope this article provides a solid foundation for understanding qualitative and quantitative research, their unique strengths, weaknesses, and their application across various fields. Remember, whether you're conducting qualitative or quantitative research, LimeSurvey has the powerful tools you need to design and carry out your study. Try LimeSurvey now, and take your research to the next level! {loadmoduleid 429}

Table Content

Definitions: two sides of the same coin.

Research: it's the beating heart of progress. It fuels innovation, sheds light on unknown territories, and informs decisions. But just as a coin has two sides, so does research: meet Qualitative and Quantitative research, the two dynamic heroes of our story.

Qualitative research , the explorer of our duo, seeks to understand the world from the participant's viewpoint. It delves into the depth of 'why' and 'how' a phenomenon occurs, providing insights into people's motivations, thoughts, and feelings.

On the other hand, Quantitative research , our numerical navigator, quantifies the data to yield measurable, statistical insights. It asks 'how much' or 'how many', and delivers results in numbers, charts, and graphs.

Both types are invaluable, both unique. And both are vital tools in the toolbox of every researcher.

Comparison table: Differences between qualitative and quantitative research

What do qualitative and quantitative research have in common?

Is quantitative research better than qualitative.

One is not better than the other —the truth, as is often the case, lies somewhere in the middle. Both are powerful in their own right, and both share a common goal: to explore, understand, and contribute to our knowledge. Choosing the appropriate method depends on your research question, objectives, and resources. They are two sides of the same research coin, both offering a wealth of insights.

Pros and Cons: When to use qualitative and quantitative research

Qualitative and quantitative research are like two arrows in a researcher's quiver, each with its own strengths and weaknesses. Understanding these can help you choose the most appropriate method for your study.

a) Advantages of Qualitative Research

• In-Depth Understanding : It's the Sherlock Holmes of research. Qualitative research probes deep into the matter to extract rich insights and unravel intricate details.
• Flexible and Adaptive : Unlike rigid survey forms, qualitative research can evolve with the study, enabling the researcher to probe emerging trends in real-time.
• Contextual : By considering the environment and social norms, qualitative research ensures a holistic view of the phenomena.
• Human-Centric : It centers on human experiences, emotions, and behaviors, making it ideal for exploratory research.

b) Limitations of Qualitative Research

• Time and Resource Intensive : Conducting interviews or observations requires substantial time, which might be a constraint for some studies.
• Subjectivity : The presence of the researcher can influence the participant's responses, potentially introducing bias.
• Non-Generalizable : The findings are context-specific and may not be applicable to the larger population.
• Requires Expertise : Analyzing qualitative data needs a seasoned researcher with a keen eye for detail.

c) Advantages of Quantitative Research

• Quantifiable : Love numbers? So does quantitative research. It provides measurable data, making it easier to identify trends and patterns.
• Replicable : The structured approach ensures that the study can be replicated, enhancing the validity of the findings.
• Generalizable : Large sample sizes allow for generalizations about the population, offering broad insights.
• Unbiased : The use of statistical techniques helps reduce bias, ensuring objectivity.

d) Limitations of Quantitative Research

• Limited in Depth : While it tells you 'how many,' it doesn't explain 'why.'
• Less Flexible : The structured format doesn't allow for probing or adapting the study based on participant responses.
• Decontextualized : Quantitative research may ignore the context, potentially oversimplifying complex phenomena.
• Requires Large Sample Size : To ensure representativeness and reduce error, quantitative research requires larger samples, which may not always be feasible.

e) How Do Qualitative and Quantitative Research Work Together?

Qualitative and quantitative research aren't adversaries—they're allies. Think of them as two pieces of a puzzle, each contributing to a comprehensive picture. For instance, a researcher may start with qualitative research to explore a phenomenon, and then use quantitative research to measure the trends observed.

f) How to Choose the Best Design?

Choosing the right design is like picking the right tool for a job—it depends on your objectives, your questions, your available resources, and your time constraints.

Remember, qualitative research is your go-to for exploring 'why' and 'how.' If you seek to understand experiences, behaviors, or the underlying reasons, qualitative is your friend.

For measuring 'how much' or 'how many,' or for studying relationships between variables, turn to quantitative research.

Consider a mixed-method approach if you want the best of both worlds, but be prepared—it demands time, resources, and expertise.

With the right tools in hand, the world of research is your oyster. Let's march on and explore the methods used to gather data in qualitative and quantitative research. Stay tuned!

Methods: How to gather data in qualitative and quantitative research

Every research endeavor begins with data collection. Both qualitative and quantitative research employ distinct methodologies that speak to their respective goals and applications.

a) Qualitative Methods

In-Depth Interviews : Like a friendly conversation, these interviews explore a participant's perspectives, experiences, and motivations in detail. They are flexible and allow for follow-up questions.

Focus Groups : Think of it as a roundtable discussion. A group of people discusses a topic, providing a variety of perspectives and interactions to analyze.

Observations : Actions speak louder than words! This method involves observing participants in their natural environment, capturing behavior that might not emerge in a formal interview.

Case Studies : A case study is an in-depth analysis of a single 'case'—it could be a person, a group, or a specific context. It's like the biography of a research subject.

Ethnography : Walk a mile in their shoes. Ethnography involves immersing oneself in the participant's environment to understand their culture, behaviors, and interactions.

b) Quantitative Methods

Surveys : A classic! Surveys can collect data from a large group using pre-determined questions, making it easier to quantify and compare responses.

Experiments : Cause-effect relationship, anyone? Experiments manipulate one variable to study its impact on another, offering conclusive evidence.

Observational Research : Observational research in a quantitative context involves systematic collection and categorization of observed data to derive statistical insights.

Secondary Data Analysis : Why reinvent the wheel? This method involves analyzing data collected by someone else. Think census data or company reports.

Longitudinal Studies : Time travel, the research way! Longitudinal studies collect data over an extended period to track changes and detect trends.

Next, let's delve into how we make sense of all this data—welcome to the world of data analysis!

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Data Analysis: How to Analyze Qualitative and Quantitative Data

After collecting data, the next crucial step is data analysis, where we transform raw data into meaningful insights. Both research types use different analytical approaches that complement their distinct objectives and data characteristics.

a) Qualitative Data Analysis

Qualitative data analysis is all about understanding the context, meaning, and patterns hidden within the data. This is often done through:

Thematic Analysis : This method involves identifying and analyzing patterns (or 'themes') within the data. Researchers meticulously go through the data, annotating and grouping segments of text by theme.

Narrative Analysis : Stories reveal a lot! In narrative analysis, researchers explore participants' narratives to understand their experiences and perspectives.

Discourse Analysis : Beyond what is said, how it is said matters. Discourse analysis looks at the language used, considering factors like sentence structure, word choice, and conversation flow.

Grounded Theory : The theory grows from the data! Grounded theory uses iterative data collection and analysis to develop theories rooted directly in the collected data.

Content Analysis : In content analysis, qualitative information (like text or media) is categorized and counted to identify patterns and frequencies.

b) Quantitative Data Analysis

Quantitative data analysis aims to quantify relationships between variables and generalize findings . This can be achieved through:

Descriptive Statistics : These give a summary of the data through measures like mean, median, mode, and standard deviation.

Inferential Statistics : Want to make predictions? Inferential statistics uses sample data to make predictions about a population or test hypotheses.

Regression Analysis : Regression models the relationship between a dependent variable and one (or more) independent variables.

Factor Analysis : Factor analysis groups related variables together, reducing the data's dimensionality and making it more manageable.

Time Series Analysis : Time matters! Time series analysis examines data points collected over time to identify trends or cycles.

Validity and Reliability

Validity and reliability are two critical considerations in both qualitative and quantitative research. These are the pillars that uphold the quality of research findings and conclusions.

Validity relates to the accuracy and truthfulness of the research findings. It's about whether the research genuinely measures what it intends to measure. In qualitative research, this is often ensured through credibility, transferability, confirmability, and dependability. In contrast, quantitative research uses internal and external validity.

Reliability refers to the consistency and repeatability of the research results. If the research were to be replicated under similar conditions, the findings should be more or less the same. In qualitative research, reliability is ensured through dependability, while in quantitative research, reliability is measured using tools like Cronbach’s Alpha.

Sample Size

The sample size in a research study can greatly influence the results. Qualitative research typically uses smaller sample sizes, as it is more focused on understanding concepts, thoughts, and experiences in-depth. On the other hand, quantitative research often requires larger sample sizes to ensure the findings' statistical significance.

The type of questions asked in qualitative and quantitative research also differ greatly.

Qualitative research questions are often open-ended, exploratory, and focus on the participants' experiences and perspectives. Here are a few examples:

• Can you describe your experience using our mobile app?
• How did you feel when you first started using our product?
• What factors influenced your decision to purchase from our brand?
• Can you tell us about a time when our customer service exceeded your expectations?
• In what ways has our product impacted your daily routine?

On the other hand, quantitative research questions are more closed-ended, looking for specific, measurable answers. Here are some examples:

• On a scale of 1-10, how satisfied are you with our product?
• How many times a week do you use our service?
• Would you recommend our product to a friend or colleague? (Yes/No)
• How much time do you spend on our website during a typical visit?
• Which feature of our product do you use the most? (Multiple choice)

These questions demonstrate the distinct objectives and outcomes of qualitative and quantitative research. Now, let's delve into real-world examples across various sectors to see these research methods in action!

Each research method has its unique merits, and this is illuminated when we look at them in specific contexts. Here's how qualitative and quantitative research can be applied across various fields:

a) Healthcare examples In healthcare, qualitative research may be used to understand patient experiences with a specific treatment, where they can share their feelings and perceptions freely. On the other hand, quantitative research can be used to measure the effectiveness of a new drug, with concrete data like patient recovery rates.

b) Nursing examples Nursing research might employ qualitative research to understand the experiences of patients living with chronic illness, gathering rich, detailed narratives. Quantitative research may be used to identify patterns, like the impact of a new hygiene protocol on the rate of hospital-acquired infections.

c) Psychology examples In psychology, qualitative research can be used for in-depth exploration of complex phenomena like stress perception. Quantitative research, on the other hand, is often used to test hypotheses, like the correlation between sleep duration and cognitive performance.

d) UX examples User Experience (UX) researchers often use qualitative research to get rich insights about a user's experience with a product, often using techniques like user interviews. Quantitative research can provide hard numbers on usage patterns, like the percentage of users who abandon their shopping carts.

e) Marketing examples Marketing teams may use qualitative research to delve into consumer attitudes towards a brand or product. Quantitative research can be used to track measurable outcomes, like the impact of an ad campaign on sales numbers.

f) Social work examples In social work, qualitative research can offer a detailed understanding of the experiences of individuals in a community. Quantitative research can provide data on larger-scale patterns, like the prevalence of unemployment in that community.

g) Sociology examples Sociologists may use qualitative research to understand the dynamics within a particular social group, with in-depth interviews. Quantitative research can provide broader trends in society, like the correlation between education levels and income.

h) Education examples Educational researchers can use qualitative research to understand the experiences and challenges of students in a classroom setting. Quantitative research can provide measurable outcomes, like the impact of a teaching method on standardized test scores.

i) Counseling examples In counseling, therapists may use qualitative research to understand a client's personal narrative better. Quantitative research can help in measuring the effectiveness of a specific therapeutic intervention.

j) Criminal justice examples Criminal justice researchers might use qualitative research to understand the experiences of individuals in the justice system. Quantitative research can provide hard data on crime rates or the effectiveness of a rehabilitation program.

k) Law examples In law, qualitative research can be used to understand the experiences and perspectives of individuals involved in a legal case. Quantitative research may be used in broader legal research to identify patterns or correlations, like the relationship between certain laws and crime rates.

Finally, let's turn our gaze towards psychology, a field where both qualitative and quantitative research play vital roles.

Psychology, as a discipline, studies human behavior and the mind. Both qualitative and quantitative research approaches contribute to its breadth and depth.

Qualitative research in psychology might involve studying a small group of individuals with a rare psychological condition, using in-depth interviews to gather rich and detailed data. It can help explore intricate phenomena such as emotions, thought processes, or experiences that are difficult to capture with numerical data.

On the other hand, quantitative research in psychology might involve testing a hypothesis about the impact of screen time on attention span across a large sample size, using structured methods like surveys or experiments. This approach allows for statistical analysis, which can highlight patterns, correlations, or cause-and-effect relationships.

Together, qualitative and quantitative research methods help paint a complete picture, providing both the detailed context and broad trends needed to advance psychological understanding.

In this blog post, we've taken a deep dive into the world of qualitative and quantitative research. We've seen that these two methodologies, while distinct, often complement each other to provide a well-rounded understanding of the research question at hand.

We hope this article provides a solid foundation for understanding qualitative and quantitative research, their unique strengths, weaknesses, and their application across various fields.

Remember, whether you're conducting qualitative or quantitative research, LimeSurvey has the powerful tools you need to design and carry out your study. Try LimeSurvey now , and take your research to the next level!

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Frequently asked questions

What’s the difference between quantitative and qualitative methods.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Comparing Quantitative and Qualitative Methods

Please see below for a description of quantitative and qualitative social science research methods.  The table below provides an outline of some of the attributes of each.

Quantitative and qualitative methods are the two main categories of empirical research.  

Adapted from: McMillan, J. H. (2012).  Educational research: Fundamentals for the consumer  (6th ed.). Boston, MA: Pearson.

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Research Design: Qualitative, Quantitative, and Mixed Methods Approaches 5th Edition

This bestselling text pioneered the comparison of qualitative, quantitative, and mixed methods research design. For all three approaches, John W. Creswell and new co author J. David Creswell include a preliminary consideration of philosophical assumptions; key elements of the research process; a review of the literature; an assessment of the use of theory in research applications, and reflections about the importance of writing and ethics in scholarly inquiry. New to this Edition

• Updated discussion on designing a proposal for a research project and on the steps in designing a research study.  
• Additional content on epistemological and ontological positioning in relation to the research question and chosen methodology and method. 
• Additional updates on the transformative worldview. 
• Expanded coverage on specific approaches such as case studies, participatory action research, and visual methods. 
• Additional information about social media, online qualitative methods, and mentoring and reflexivity in qualitative methods. 
• Incorporation of action research and program evaluation in mixed methods and coverage of the latest advances in the mixed methods field
• Additional coverage on qualitative and quantitative data analysis software in the respective methods chapters. 
• Additional information about causality and its relationship to statistics in quantitative methods. 
• Incorporation of writing discussion sections into each of the three methodologies. 
• Current references and additional readings are included in this new edition.
• ISBN-10 1506386709
• ISBN-13 978-1506386706
• Edition 5th
• Publication date January 2, 2018
• Language English
• Dimensions 7 x 0.75 x 10 inches
• Print length 304 pages
• See all details

Editorial Reviews

About the author.

John W. Creswell, PhD, is a Professor of Family Medicine and Senior Research Scientist of

the Michigan Mixed Methods Program. He has authored numerous articles and 34 books on

mixed methods research, qualitative research, and research design. While at the University of

Nebraska–Lincoln, he held the Clifton Endowed Professor Chair, served as Director of the

Mixed Methods Research Office, co-founded SAGE’s Journal of Mixed Methods Research , and

was an Adjunct Professor of Family Medicine at the University of Michigan and a consultant to

the Veterans Administration Health Services Research Center in Ann Arbor, Michigan. He was

a Senior Fulbright Scholar to South Africa in 2008 and to Thailand in 2012. In 2011, he co-led

a National Institutes of Health working group on the “best practices of mixed methods research

in the health sciences,” served as a Visiting Professor at Harvard’s School of Public Health and

received an honorary doctorate from the University of Pretoria, South Africa. In 2014, he was

the founding President of the Mixed Methods International Research Association. In 2015, he

joined the staff of Family Medicine at the University of Michigan to Co-Direct the Michigan

Mixed Methods Program. In 2017, he coauthored the American Psychological Association

“standards” on qualitative and mixed methods research. The fourth edition of this book on

Qualitative Inquiry & Research Design won the 2018 McGuffey Longevity Award from the U.S.

Textbook & Academic Authors Association. During the COVID-19 pandemic, he gave virtual

keynote presentations to many countries from his office in Osaka, Japan. Updates on his work

can be found on his website at johnwcreswell.com.

Product details

• Publisher ‏ : ‎ SAGE Publications, Inc; 5th edition (January 2, 2018)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 304 pages
• ISBN-10 ‏ : ‎ 1506386709
• ISBN-13 ‏ : ‎ 978-1506386706
• Item Weight ‏ : ‎ 1.2 pounds
• Dimensions ‏ : ‎ 7 x 0.75 x 10 inches
• #9 in Social Sciences Methodology
• #16 in Social Sciences Research
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About the author

John w. creswell.

John W. Creswell is a Professor of Educational Psychology at Teachers College, University of Nebraska-Lincoln. He is affiliated with a graduate program in educational psychology that specializes in quantitative and qualitative methods in education. In this program, he specializes in qualitative and quantitative research designs and methods, multimethod research, and faculty and academic leadership issues in colleges and universities.

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• Published: 30 August 2024

A qualitative investigation of financial decision-making and enabling factors among ethnic minority young adults in Hong Kong

• Esther Yin-Nei Cho 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1113 ( 2024 ) Cite this article

Metrics details

Current understanding of financial decision-making among racial/ethnic minority young adults is limited: day-to-day financial decisions of racial/ethnic minorities are underexamined, younger racial/ethnic minorities receive limited attention, studies on racial/ethnic minorities are mainly conducted in Western societies, and research on financial literacy and decision-making is predominantly quantitative in nature. Against this backdrop, this study utilized a qualitative approach to examine a range of financial decision-making among ethnic minority young adults in Hong Kong, including personal budgeting, spending, financial planning, the use of financial products, debt management, and the detection of financial fraud. Individual interviews were conducted with 53 Pakistani, Indian, Nepalese, and Filipino participants aged 18 to 29 who employed various budgeting strategies and faced challenges. Their spending was modest, and they espoused various spending philosophies. Many saved approximately one-third of their income using saving tactics and setting financial goals, and investing in both Hong Kong and their home countries. Informal borrowing was common, though some sought alternative loans. One-third used credit cards, with accompanying occasional risks. Despite employing protective tactics, they still fell victim to scams. Factors facilitating their financial decision-making include family social capital, intrapersonal characteristics, social dynamics factors, command of knowledge, and facilitative contextual circumstances. This study addresses knowledge gaps by providing an in-depth understanding of financial decision-making among ethnic minority young adults in a non-Western context. It has significant implications for timely and tailored financial literacy education for minority societal members.

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Introduction.

Extensive attention has been given to studying and promoting financial literacy, as shown by the burgeoning literature on the subject (e.g., Angrisani et al., 2021 ; Atkinson and Messy, 2012 ; Kempson, 2009 ; Lusardi and Mitchell, 2007 ), financial literacy incorporated as a national priority (OECD, 2015a ), and the proliferation of financial education programs worldwide (Kaiser and Menkhoff, 2020 ). The primary reason for this attention and effort lies in the growing awareness of the generally low levels of financial literacy across the world (e.g., Lusardi, 2019 ; Lusardi and Mitchell, 2011a ) and its impact on financial well-being, which, in turn, influences overall individual and societal well-being (Grohmann et al., 2018 ).

There is no universal definition of financial literacy, but the definitions used in the literature are essentially similar (e.g., Atkinson and Messy, 2012 ; Hung et al., 2009 ; OECD, 2015b ). For instance, financial literacy is defined as the “knowledge of basic economic and financial concepts, as well as the ability to use that knowledge and other financial skills to manage financial resources effectively for a lifetime of financial well-being” (Hung et al., 2009 , p.12) or “a combination of awareness, knowledge, skill, attitude, and behavior necessary to make sound financial decisions and ultimately achieve individual financial well-being” (Atkinson and Messy, 2012 , p.14). The terms “financial literacy” and “financial capability” are often used interchangeably (e.g., Muir et al., 2017 ; Xiao et al., 2014 ), referring to the ability to apply appropriate financial knowledge and engage in financial behaviors to achieve financial well-being (Xiao et al., 2014 ), though it may also include access to financial resources (Johnson and Sherraden, 2007 ).

These slightly different definitions converge around three interrelated ideas. First, financial literacy consists of basic elements, such as knowledge, attitudes, skills, and behavior, necessary for making financial decisions. Second, it involves the ability to apply these elements for sound financial decision-making. Third, financial literacy ultimately affects financial well-being through improved financial decision-making. Therefore, the extent of individuals’ financial literacy is not merely determined by their knowledge but also by how well they apply knowledge in their decision-making, which requires practice and judgement (Worthington, 2006 ). To fully understand people’s financial literacy, it is also necessary to examine their financial decisions in terms of both their practice and perceptions.

Disparities in financial literacy among different population groups have been documented based on characteristics such as age, gender, education, race/ethnicity, income level, and marital status (e.g., Brown and Graf, 2013 ; Lusardi et al., 2010 ; Lusardi and Mitchell, 2011a , 2011b ). Racial/ethnic minorities comprise one of the most vulnerable groups (Al-Bahrani et al., 2019 ; Angrisani et al., 2021 ; Brown and Graf, 2013 ; Lusardi and Mitchell, 2011b ). For example, Black and Hispanic individuals in the USA tend to score lower on financial literacy questions than Whites (Lusardi and Mitchell, 2011b ). While it is recognized that racial/ethnic minorities have lower levels of financial knowledge, a more comprehensive understanding of their financial literacy is still needed.

First, racial/ethnic minorities’ financial decision-making has not been sufficiently examined. Existing studies focus on banking accounts (Barcellos and Zamarro, 2021 ; Kim et al., 2016 ; Lusardi, 2005 ), credit use and debts (Ekanem, 2013 ; Gaur et al., 2020 ; Goodstein et al., 2021 ; Yao et al., 2011 ), asset holding (Lusardi, 2005 ), and retirement planning (Kim et al., 2021 ). Other important day-to-day financial decisions are less understood, such as budgeting, savings, using other financial products, and detecting financial fraud.

Second, younger members of racial/ethnic minorities, who face double challenges, have received limited attention. As racial/ethnic minorities, they already have low levels of financial literacy. As younger adults, they are more financially vulnerable than their older counterparts. Not only do they have lower levels of financial knowledge, such as inflation, compound interest, and risk diversification (Lusardi et al., 2010 ; Lusardi and Mitchell, 2011a ), but they also face more financial challenges due to longer life spans, more financial decisions to make, and greater financial risks in an increasingly complex global financial environment.

Third, most studies on racial/ethnic minorities have been conducted in Western societies, particularly the USA, with more limited research conducted in other contexts, such as Asian societies.

Fourth, research on financial literacy is predominantly quantitative (Goyal and Kumar, 2021 ; Kelley et al., 2021 ). Qualitative studies are few, particularly regarding racial/ethnic minorities (Ekanem, 2013 ; Gaur et al., 2020 ). While quantitative studies provide a broad perspective on the subject matter, qualitative studies offer an in-depth understanding of how individuals perceive and make meaning of their financial decisions. This information is valuable for informing financial literacy education and thus improving financial decision-making.

This study examined a range of financial decisions made by ethnic minority young adults in Hong Kong using qualitative inquiry to address these limitations. By adopting a qualitative approach, the study focuses on generating themes that may not be captured in quantitative studies relying on statistical figures. It extends the literature by providing a deeper understanding of the financial decisions made by younger ethnic minority individuals within a non-Western context. The findings also reveal factors facilitating sound financial decision-making among ethnic minority younger people, particularly in Hong Kong. These findings have important implications for tailoring “just-in-time” financial literacy education to meet their specific needs, as opposed to a “one-size-fits-all” approach (Goyal and Kumar, 2021 ).

In the following, after briefly highlighting the relevant literature on financial literacy and financial decisions, the findings on ethnic minority young adults regarding different areas of financial decision-making will be presented. Factors facilitating their financial decision-making will be identified, and implications for financial literacy education and further research discussed.

A brief overview of financial literacy and financial decisions

Patterns of financial literacy.

Financial literacy levels are associated with various socioeconomic factors, including age, gender, education, parental education, employment status, marital status, area of residence, and race and ethnicity. Financial literacy exhibits a bell curve distribution with age. It is lower among young and old individuals than those in the middle of the life cycle (Atkinson and Messy, 2012 ; Brown and Graf, 2013 ). For instance, less than one-third of young adults possess basic concepts about inflation, risk diversification, and compound interest (Lusardi et al., 2010 ; Lusardi and Mitchell, 2011a ). Regarding gender, women tend to have lower levels of financial literacy than men. They have less financial knowledge about debt, inflation, risk diversification, and compound interest (Brown and Graf, 2013 ; Lusardi and Mitchell, 2011a ; Lusardi and Tufano, 2015 ) and are less likely to plan for retirement (Herd et al., 2012 ). Educational attainment is positively related to financial literacy (Herd et al., 2012 ; Klapper et al., 2012 ; Lusardi and Mitchell, 2007 , 2011a ). Less well-educated people are less likely to answer financial literacy questions correctly and tend to indicate not knowing the answer (Lusardi and Mitchell, 2011a ). Individuals without a college degree are less likely to understand concepts about inflation, risk diversification, and simple interest calculations (Herd et al., 2012 ). More educated people tend to have positive attitudes towards retirement planning (van Rooij et al., 2011a ) and possess a bank account (Klapper et al., 2012 ). Parental education, particularly mothers’ education, and parents’ possession of stock or retirement accounts are positively related to financial literacy (Lusardi et al., 2010 ). Fathers’ education is positively associated with their daughters’ financial literacy (Mahdavi and Horton, 2014 ). In terms of employment status, employed individuals have substantially higher levels of financial literacy than those who are unemployed or retired (Brown and Graf, 2013 ; Bucher-Koenen and Lusardi, 2011 ). Marital status is also related to financial literacy levels, with married people tending to have higher levels than single individuals (Brown and Graf, 2013 ). In terms of area of residence, those who live in a city score better in financial knowledge than their rural counterparts (Klapper and Panos, 2011 ). As for race and ethnicity, individuals belonging to the racial majority or being native-born have higher financial literacy levels (Brown and Graf, 2013 ; Lusardi and Mitchell, 2007 , 2011b ). For example, African Americans and Hispanics in the USA have lower financial literacy than Whites (Al-Bahrani et al., 2019 ; Lusardi and Mitchell, 2007 , 2011b ). In Switzerland, foreign citizens, especially immigrants with a non-German native language, exhibit lower financial literacy than native-born individuals (Brown and Graf, 2013 ). Students from an immigrant background also have lower financial literacy than other students (OECD, 2014 ).

The association between financial literacy and financial decision-making

Financial literacy is crucial for making sound financial decisions and avoiding costly mistakes. Empirical findings show that financial literacy is associated with various outcomes of financial decision-making, including day-to-day financial management, financial planning, using financial products, debt management, and detecting financial scams.

First, financial literacy can lead to better day-to-day financial management, such as responsible family budgeting, timely bill payments, and avoiding impulsive purchases (Akben-Selcuk, 2015 ; Atkinson and Messy, 2012 ; Hilgert et al., 2003 ; Perry and Morris, 2005 ). For example, a study of college students shows that those with higher financial literacy are more likely to pay bills on time and have a budget in place (Akben-Selcuk, 2015 ). Second, financial literacy is associated with better financial planning. Financially literate individuals are more likely to engage in savvy and active saving behavior (Akben-Selcuk, 2015 ; de Bassa Scheresberg, 2013 ; Deuflhard et al., 2019 ; Klapper et al., 2012 ). They are more likely to plan for retirement and save for emergencies (de Bassa Scheresberg, 2013 ). Third, financial literacy is related to better debt management. Individuals with better debt literacy will avoid high-cost borrowing, high transaction costs, and higher fees (Lusardi and Tufano, 2015 ; de Bassa Scheresberg, 2013 ). They also adopt better credit card behavior, which minimizes fees and interest charges resulting from late payments, cash advances, and paying only the minimum amount due (Lusardi and Tufano, 2015 ; Mottola, 2013 ). Fourth, financial literacy influences the use of financial products. Individuals with higher levels of financial literacy are less likely to be unbanked and use alternative financial services, such as payday loans (Kim and Lee, 2018 ). It is also associated with greater participation in investment and savvy investment decisions. Financially literate people tend to evaluate financial products carefully (Atkinson and Messy, 2012 ) and be more involved in the stock market (Almenberg and Dreber, 2015 ; van Rooij et al., 2011b ). Finally, financial literacy can increase the ability to detect financial fraud. Findings show that a one standard deviation increase in financial knowledge increases the probability of fraud detection by three percentage points (Engels et al., 2020 ).

Financial decisions of racial/ethnic minorities

Research on financial decisions made by racial/ethnic minorities has mostly focused on using financial products, debt management and credit use, and retirement planning.

Regarding financial products, African Americans and Hispanics in the USA are less likely to have a bank account and hold asset investments, such as stocks, than Whites (Kim et al., 2016 ; Lusardi, 2005 ; Shanbhag, 2022 ). Another study examined community development credit unions providing affordable financial services, such as mortgages, to help African Americans save money and build assets (Nembhard, 2013 ). Newly arrived immigrants in Australia demonstrated low utilization of financial products and services, such as ATM cards, bank savings accounts, and credit cards (Zuhair et al., 2015 ).

As for debt, around 80% of Chinese American respondents in a study on debt ownership held some type of debt, such as credit cards, mortgages, and instalment loans (Yao et al., 2011 ). Age, the presence of children under 18, health, income, and amount of financial or non-financial assets are associated with the probability of borrowing. Other studies have explored the attitudes of Black and other minority ethnic entrepreneurs experiencing bankruptcy in England (Ekanem, 2013 ) or Pacific Island adults in New Zealand towards debt, money, or bankruptcy (Gaur et al., 2020 ). Significant racial/ethnic differences in credit use have also been reported. Approximately 75% of White, 80% of Asian, 50% of Hispanic, and 45% of Black households use bank credit, in terms of a credit card or a personal loan or line of credit from a bank. However, nonbank credit, such as payday loans, is more predominant among Black and Hispanic households (Goodstein et al., 2021 ).

Studies on retirement planning show that ethnic minorities have less savings for retirement than Whites (Gough and Adami, 2013 ) and are also less motivated to hold retirement savings even after controlling for different socio-economic characteristics (Kim et al., 2021 ).

Ethnic minorities in Hong Kong

Despite growing efforts to promote financial literacy among people in Hong Kong in recent years, such as establishing the Investor and Financial Education Council as a public organization to promote financial education in Hong Kong, ethnic minority younger adults are still largely overlooked. In terms of research, the financial literacy of ethnic minority young adults or even ethnic minority communities is underexplored. Existing survey findings only show disparities in financial literacy between younger and older adults in the general population, with the former performing poorly, particularly in timely bill payments, making ends meet without borrowing, and keeping up with their financial affairs (Investor Education Centre, 2018 ). Financial education programs targeting ethnic minorities are also limited. Only 0.6% of the 661 financial education initiatives conducted between 2011 and 2015 were intended for the ethnic minority population (Investor Education Centre, 2015 ).

Ethnic minorities in Hong Kong refer to the non-Chinese population, which makes up 8.4% of the total population (Census and Statistics Department, 2022 ). Most are Filipino and Indonesian, constituting 32.5% and 22.9%, respectively, and most (more than 90%) of these are foreign domestic helpers living in their employers’ homes. South Asians, including Pakistanis, Indians, and Nepalese, make up 16.5% of the ethnic minority population. The rest are mostly White people and other Asians, such as Korean and Japanese, who often enjoy a higher social and economic status in the city and are not the focus of this study. After excluding the Filipina and Indonesian domestic helpers, Pakistanis, Indians, Nepalese, and Filipinos represent the largest proportion of the ethnic minority population in Hong Kong and are the focus of this study. These individuals may have migrated to Hong Kong with their families or were born in Hong Kong. Some of them may have acquired a certain level of English and Cantonese, the local language, especially if they have received education in Hong Kong.

In Hong Kong, ethnic minority younger adults are likely to perform less well in financial literacy and financial decisions than the general population, which is largely made up of ethnic Chinese. This is because they generally fare poorly in terms of education and employment. For example, school attendance rates for ethnic minorities in the age groups 3–5, 12–17, and 18–24 years were 90.7%, 96.2%, and 29.2%, respectively, compared to 92.5%, 97.8%, and 51.8% for the whole population in 2016 (Census and Statistics Department, 2017 ). As regards occupation, 35% of Nepalese, 35% of Pakistani, and 25% of Indian individuals were engaged in elementary jobs, such as cleaners, laborers, and food preparation assistants, compared to 21% of the general population (Census and Statistics Department, 2017 ).

Drawing on the literature, this study examined the financial decision-making of ethnic minority young adults in Hong Kong and posed the following research questions:

What are the experiences of ethnic minority young adults, in terms of practice or strategies and perceptions, in different areas of financial decision-making, including day-to-day financial management (personal budgeting and spending), financial planning, using financial products, debt management, and detecting financial fraud?

What factors enable ethnic minority young adults to make sound financial decisions?

This study employed a qualitative approach, using individual in-depth interviews, to examine the financial decision-making of ethnic minority young adults in their daily lives. The rich data gathered from qualitative inquiry can provide a nuanced understanding of human behavior, which involves practice and judgement. Semi-structured interviews allow participants to express their thoughts in their own words, which is particularly beneficial for delving into a poorly understood topic.

Participants and data collection

In this study, Pakistani, Indian, Nepalese, and Filipino participants were recruited through NGOs that provided services for ethnic minority young adults and international offices of universities using purposive sampling. In addition to ethnicities, young adults who were 18 to 29 years old, permanent residents of Hong Kong, and students or employed were recruited. Table 1 summarizes the participants’ background characteristics. Fifty-three ethnic minority young adults aged between 18 and 29 years were recruited: 16 Pakistani, 13 Indian, 13 Nepalese, and 11 Filipino. Thirty-five were aged 18–23, and 18 were aged 24–29. There were 30 males and 23 females. Thirty-two were employed at the time of the interview, and 21 were students. The occupations of those in employment included elementary jobs (e.g., security guards), service workers (e.g., customer service), associate professionals (e.g., program workers in NGOs), and professionals (e.g., software developers). Many ethnic minority students had part-time jobs ( n  = 12), such as cashiers, tutors, football coaches, and delivery workers. Other students depended on their parents for financial support ( n  = 9). Most participants were pursuing or had attained at least a bachelor’s degree ( n  = 30). Sub-degree education being pursued or attained included associate degrees, higher diplomas, or foundation diplomas ( n  = 11). The education level of the remaining participants ranged from Secondary 3 to 6 ( n  = 12). Students’ monthly earnings ranged from US$90 to $2500; more than half received US$1250 or less ( n  = 18). Working participants’ monthly earnings ranged from US$625 to $3560, most receiving between US$1250 and $2500 ( n  = 16).

The Research Ethics Committee of the university to which the author was affiliated provided ethical approval before the study commenced. Before interviewing, participants’ informed consent was obtained after explaining the study’s objectives and principles of confidentiality and voluntary participation. Each interview took place in an NGO or university and generally lasted between 60 and 75 min.

Interview questions

The interview questions were developed to gather information on a range of financial decisions based on the literature on financial literacy and financial decisions, including day-to-day financial management (personal budgeting and spending), financial planning, using financial products, debt management, and detecting financial fraud. Participants were asked about their practices or strategies and perceptions of each area of financial decision-making. The interviews were conducted in English.

The audio recordings of individual interviews were transcribed verbatim. Following Braun and Clarke ( 2006 ), thematic analysis was employed to identify, analyze, and report major themes within the data. The researchers first familiarized themselves with the data through repeated readings. They then developed initial codes to capture the meaningful aspects of the data. These codes were further organized into potential themes, and the relevant data associated with each code were collated within the potential themes. The potential themes were refined through careful review to ensure the coherence of data within each of them and that they were distinct. Once the refinements were finalized, the themes were named to accurately reflect their essence.

The following findings present the practices, strategies and perceptions of various financial decisions. Table 2 summarizes the major themes., which also align with particular components of financial literacy, including financial knowledge, attitudes, and behavior (Atkinson and Messy, 2012 ).

Personal budgeting

Spending- or saving-centric approach in practice.

Most participants had developed habits of monthly budgeting, using saving- or spending-centric approaches. The former involves setting a savings amount and then spending the remainder, whereas the latter involves setting a spending limit and then saving the remainder. For example, one participant was more conscious of his savings:

I set in mind that every month I have a certain percentage to be saved and not to be touched. The rest is like spendable expenses, so I don’t have to go crazy saving mode. (18Indian, M/26 y, W, Degree) Footnote 1

Digital tools, parental monitoring, and mental bucketing as strategies

Participants employed various strategies in practice, including digital tools, parental monitoring, and mental bucketing. The digital tools they utilized included budgeting apps, Excel spreadsheets, online banking, and calendar or note taking apps in their phone. Some examples of budgeting apps to keep track of budgets were Zoho Expense, Ahorro, Mobills, Money Manager, Spendee, and Savings Planner.

I have this app Mobills …I just type all my expenses in where I spent the money so it helps me track if I have exceeded the monthly limit. (27Nepalese, M/20 y, S, Degree)

Some participants relied on online banking, e-statements, or Excel spreadsheets to keep track of their budgets. One participant primarily used credit cards for spending: “I check my monthly statement and like …oh, this month I spent more on food. I should cut it down a bit.” (18Indian, M, WA, 26 y, Degree) Another participant updated his budget sheet almost daily, “I make sure I don’t cross the budget for daily food expenditures, so I separate expenditures of breakfast, lunch, and dinner. (35Filipino, M/28 y, W, S7)

Parental monitoring in budgeting was common, where parents set spending limits or kept the money to prevent their children from overspending. Participants were positive about parental monitoring:

I gave all my earnings to my mom and she’ll help save for me. After deducting the savings, she’ll allocate some for my spending. (11Pakistani, M/20 y, S, S6)

They won’t let me spend my own money so that they can keep track of what I do. …I’ll always show my mom what I bought. …I can say it is ‘control’ …but it’s good to have monitoring. (1Indian, F/23 y, S, Master’s)

However, some participants simply allocated money into different categories mentally. One said, “I don’t like keeping notes. Everything is in my mind.” (47Indian, M/18 y, W, S4), while another responded, “I just keep them in my mind, divided by categories.” (40Nepalese, F/21 y, W, SD)

Parental influence and experiential learning

Participants indicated that they acquired budgeting ideas through observing their parents and learning by doing, especially after earning their first income.

I’m learning from him [father], like how to save up money, how to spend it wisely, and how to spend it on only the important things and not to waste the money. (2Pakistani, M/22 y, S, SD)

It was around my university years when I was doing a part-time job and earning some money …my own concept of saving started to form. (18Indian, M/26 y, W, Degree)

Perceptions of budgeting

Most participants perceived budgeting positively, agreeing that it could provide a sense of control against overspending, as one said, “If you don’t have a budget, it’s really easy to overspend on stuff and you can’t control your money.” (47Indian, M/18 y, W, S4). Budgeting was also seen as a form of psychological restraint, evoking a sense of guilt when budget limits were exceeded, and fostering discipline for conscious spending:

It makes me feel guilty …kind of a warning …a yellow light that you’re spending more than you’re supposed to. … It’s psychological when I see a big number in the amount of expenses. (29Filipino, F/22 y, S, Degree)

However, many expressed the difficulty of maintaining a budgeting habit due to economic and personal challenges. Limited funds and high living costs posed economic challenges as there was not a lot of money to go around, as expressed by one participant, “I don’t really have a lot of money. …I don’t know how I can track it.” (4Indian, M/21 y, S, Degree) Another said, “It’s very difficult because nowadays all the things are pricey, but you just have a limited amount of money.” (28Filipino, F/19 y, S, S6)

Personal challenges relate to feelings that budgeting is demanding, requires much self-discipline, and causes stress. Some participants found it demanding as it was time-consuming and involved excessive work.

It sounds, you know, ridiculous to me …somewhat a waste of time. There’s a lot of data. (15Pakistani, F/25 y, S, Degree)

It’s tedious and it takes time to write down all the details. …The effort needed to keep track of things demotivates me. (34Indian, M/30 y, W, Degree)

Others found it difficult because they struggled with self-control. One said, “I want to buy many things like this and that. …It’s hard for me as I could not control myself.” (42Nepalese, F/25 y, W, SD) Others were reluctant to budget because it induced too much stress:

I think budgeting gives me a lot of stress. I just want to focus on making money, so I don’t have to worry about it. (31Filipino, M/22 y, S, Degree)

Constantly checking is kind of torturing me. …If I check it too much, I’ll get sad about my expenditure. (18Indian, M/26 y, W, Degree)

Nevertheless, a smaller proportion of participants who showed qualities such as determination and mathematical competence did not find budgeting as hard.

It’s just the willpower of a person. I don’t think there’s too much difficulty for me. (32Indian, M/29 y, W, Degree)

I have a strong mathematical background since I studied math a lot, so I don’t think numbers are a problem for me. (17Nepalese, M/18 y, S, Degree)

Spending decisions

Modest spending.

Many participants appeared to spend modestly and consciously. They generally allocated a higher portion of their budgets to basic needs. As many lived with their parents, they mostly spent money on food and transportation. Other major expenses included tuition fees, financial support for their families, rent, and personal entertainment.

I’d say 50% goes toward my food. …Insurance and everything, I’d say 20%. (38Indian, M/23 y, W, SD)

Half of my money goes to food and transportation, and the other half I’m saving for school fees and all that. (2Pakistani, M/22 y, S, SD)

Deferred purchase, bargain shopping, and one-time payment as strategies

Strategies in spending decisions included deferred purchases, bargain shopping, and one-time payments. One strategy employed was to re-evaluate spending decisions by deferring purchases:

When I shop, I double-think. … I’ll buy it a day or two later …to think about if I truly need it or not. (22Pakistani, F/25 y, W, S6)

Another common strategy was bargain shopping. Participants described how they bargain-hunted or waited for sales to get the best value for money.

If I go to buy a pair of shoes, then I like to go through the whole mall and see, you know, which one is really worth the money. (4Indian, M/21 y, S, Degree)

I’ll try my best to use as little money as I can. …I’ll check where I can get it the cheapest. (51Pakistani, M/25 y, W, Degree)

When making purchases, most preferred a one-time lump sum payment to avoid interest charges. One participant talked about the extra charges:

I used to buy in instalments with credit cards and I spent a lot, and I couldn’t pay some of the bills….Now I pay in lump sum, I find this very clear to your mind. …Nobody is calling you to pay for the minimum. (43Nepalese, M/28 y, W, Degree)

However, some paid in instalments, incurring interest on expensive products or when the budget was tight. Generally, an item costing more than HK$1000 (approximately US$125) was considered expensive.

If it’s around HK$500–$1000 (US$63–$125), I’ll spend a lump sum. But if it’s HK$5000 or HK$6,000 (US$625 or $750) like that, I’ll usually spend it on instalment. …When making it 12 months, I only need to pay HK$500. (51Pakistani, M/25 y, W, Degree)

Spending philosophies

Participants shared their perceptions about spending, revealing various spending philosophies such as differentiating between needs and wants, viewing spending as a work incentive, and embracing YOLO (You Only Live Once) spending. Conscious spenders distinguished between must-haves and nice-to-haves, ensuring they spent on what was necessary rather than what was desired.

When you buy something …you have to ask yourself whether you need it or want it, like you just think it’s cute. (25Pakistani, F/19 y, S, Master’s)

I often question whether I really need it …especially when it comes to luxury items like clothes and shoes. But for food, I do not compromise; for health, I do not compromise. (17Nepalese, M/18 y, S, Degree)

Some participants showed that spending was a motivation to work hard, as one said: “I base how much I work on my expenses. If I have many expenses coming up, I’ll try to work more.” (3Indian, M/21 y, S, Degree). Others embraced YOLO spending as a means of seeking happiness:

I didn’t want to decide how I was going to spend it. …You should never restrict yourself. Of course, you have savings. But for your spending, you should just go with whatever makes you happier. (18Indian, M/26 y, W, Degree)

You only live once! … It’s good to spend a little bit on something expensive. …With the money I earned …I deserve at least some to use on myself. (32Indian, M/29 y, W, Degree)

Financial planning

Savings habits and setting savings goals.

Many participants established a habit of saving. More than half said they allocated at least 30% of their monthly salary or pocket money to savings. Some started to save in childhood, but many did so after their first employment while their earnings served as resources for hands-on learning. One participant said, “When I started to earn my own money, I didn’t want to spend all of it. I want to save and learn about investments.” (29Filipino, F/22 y, S, Degree)

Depending on their life stage, those who saved set various saving goals. In addition to saving for education, some saved to buy property, start a family, build a business in their home country, or for retirement. Some described the goals:

I’ve always wanted to start a piggery business. …In the Philippines, …a full roasted pig we call it Lechon. It’s in every celebration. …There’s a market for that. …I want to start one because my uncle, sisters, and brothers are good at that. (29Filipino, F/22 y, S, Degree)

For the very long term, like for retirement, I’m setting aside 20% of my salary to invest in stocks and bonds. (34Indian, M/30 y, W, Degree)

Gaining financial autonomy was also mentioned as a goal, as recounted by one participant:

I’m never going to focus too much on my future husband. I’m not going to be financially dependent on another person. …I always thought …I’d get educated and then earn money. I’d not be together [with someone] and be scared of splitting just because of money. (10Nepalese, F/21 y, S, Degree)

External restraints and personal tricks as strategies

Participants employed various savings strategies. In addition to saving money in bank accounts, they used external assistance or restraints as strategies by having their parents or boyfriend save it for them:

My parents don’t want me to be spoiled with so much money. We often see that people who start earning money do some bad stuff, like getting into drugs, gaming, or going out with friends a lot. …I’m not doing all these. …But still, they keep my money. (49Filipino, M/19 y, W, S6)

I’ll give half of it [salary] to my boyfriend so I won’t be able to touch it. The remaining is for my spending. …He has a better concept of saving than me …and he helps me save. (16Indian, F/27 y, W, Degree)

Participants reported using various personal hacks, such as opening separate bank accounts for specified uses:

I have two different bank accounts. One is strictly for saving money. …The other one is for paying bills and spending on things like necessities. (35Filipino, M/28 y, W, S7)

When I started my job, I only had a Hang Seng bank account. Then I specifically opened an HSBC account to keep my education savings there. …If you see a large amount of money, it makes you less intelligent about your expenditure because it projects an illusion that you have a lot of money. (48Indian, M/24 y, W, Degree)

Another example was a four-wallet strategy to divide money into smaller portions for designated purposes:

One wallet is for saving money. …If I buy something and I get some money left, I put it in the second wallet. The third wallet is for putting money that I couldn’t touch, like paying for my violin lessons and the dentist. …The fourth wallet is for transportation. (30Filipino, F/21 y, W, SD)

Another strategy was simply stashing cash away under the mattress or in other hidden places to reduce its accessibility:

I have like HK$10,000 (US$1250) under my mattress. Every month I have HK$1000 (US$12.5) …a hundred of $10 s …put inside my mattress, and I would sleep on it. My goal is …to the point if I can’t sleep properly, I have enough money. …Some people have their piggy bank I have my mattress. (31Filipino, M/22 y, S, Degree)

Sometimes I took all the money out and put it in a more hidden place like I can literally forget about it. (28Filipino, F/19 y, S, S6)

Perceptions of financial planning

Most participants agreed that financial planning and saving were important. Some thought of it as a grown-up responsibility. As adults, they were responsible for making financial plans and avoiding irresponsible purchases:

I already feel ashamed that I’ve been asking for pocket money from my parents. …I think it’s because we’re Asian, …we depend a lot on our parents. I don’t really want to live like that. …I want to be able to stand up on my feet. (10Nepalese, F/21 y, S, Degree)

I feel like after turning 18, …it’s important to budget your money, save it, invest it, and not make stupid and foolish purchases. (19Indian, M/19 y, S, Degree)

Financial planning was seen as a safeguard against financial shocks, offering a sense of emotional wellness or peace of mind as they knew they had backup resources. This was particularly important after they experienced the COVID-19 pandemic and became motivated to be well-prepared:

It taught me that no job is stable …even pilots get laid off. …Your income is not always there. You always have to be prepared for it. (41Nepalese, M/22 y, W, Degree)

You can have a fire break out in your house, you can have your stuff get stolen, you can get hit by cancer, and even this pandemic. So financial planning is extremely important. (21Pakistani, F/23 y, W, Degree)

It’s like a comfort …in case anything goes wrong in your life. It’s always good to have a backup plan …and you always have something to protect you. (35Filipino, M/28 y, W, S7)

Nevertheless, not all participants were positive about financial planning; some valued income generation over saving money:

Saving isn’t super important to me because I feel I should be earning more than I should be able to save. …If I am earning more, I don’t have to worry about saving. (31Filipino, M/22 y, S, Degree)

Some also felt they were not good at saving because of inadequate self-discipline and limited money: “The reality …is that my income is really not a lot at all. And I recognize there’s a limit on how much I can stretch, even if I really want to stretch it.” (36Filipino, M/26 y, W, Degree)

The use of financial products

A diverse range of financial products.

Participants reported using various financial products or investments. Insurance was most frequently mentioned, followed by stocks, and other choices, including cryptocurrency, index funds, and forex trading. Buying property or gold in their home country as conventional investments was popular, as the older generations have done.

Different types of insurance, including life, medical, accident, and critical illness, were purchased and considered safe and flexible:

My insurance is three years old. …If something bad happens, I can use it. If I don’t use it after 20 years, it’s my money, so it’s like a saving. (43Nepalese, M/28 y, W, Degree)

All of them are index funds because the management cost is low and it’s simple to set up. It’s set and forget, no need to actively manage. (34Indian, M/30 y, W, Degree)

Buying property or gold in the home country was popular. Like their parents, they made or were planning to make these investments as they believed their value would steadily increase:

Dad bought properties in Pakistan and the values increase every year as it’s on the main side of the road. …The more convenient the properties, the higher the price it is. …Three are under my name, others under my siblings’ name. (22Pakistani, F/25 y, W, S6)

Buying property is safer compared to stocks. …Buying it overseas is a lot safer …because the property in Hong Kong is a lot more expensive. (31Filipino, M/22 y, S, Degree)

I can use this gold in my wedding … it’s kind of holding money because I don’t think the gold price will drop. It’s a good investment. (53Nepalese, F/25 y, W, Degree)

Parental support, peer mentoring, and self-education as strategies

Participants employed various means of obtaining information and experiencing financial products, including parental support, peer assistance, and self-education with online resources. Some parents were supportive by providing funds for hands-on learning in stock investment or opportunities for joint investment:

My dad gave me a small amount of money just to learn. …Because the only way you can learn is you do it yourself. …He helped me set up my account and everything and then I started. (19Indian, M/19 y, S, Degree)

I invested with the help of my mom. …She invested and got a return and she gave me the interests. (10Nepalese, F/21 y, S, Degree)

Assistance from financially savvy friends was also a way to enhance their knowledge and gain experience in stocks, insurance, or setting up a business:

I have a group of friends and we all invest in stocks. We like to give each other tips like, “I’m going to invest in this …maybe you should take a look at this.” Or sometimes before they invest, they ask “What do you think about this company?” And then I do my research …like we help each other. (19Indian, M/19 y, S, Degree)

My friends are in Pakistan …their family has been investing in property and stocks. …They bought their own shisha lounge recently. …I discussed with them: What was the cost? How much should I save for starting up this kind of stuff? (22Pakistani, F/25 y, W, S6)

Another strategy was self-education, by reading news, studying company information, and surfing the internet and YouTube for tutorials and knowledge:

I see the performance of their company around 5 or 10 years. Then I see the future analysis …how the company will perform in the future. (13Pakistani, M/23 y, W, Degree)

There are a lot of tutorials online or on YouTube. … There are also a lot of good pages that talk about investing. … It’s easily obtainable. (10Nepalese, F/21 y, S, Degree)

Perceptions of using financial products

Participants expressed different views about using financial products. The favorable view held that financial products acted as a passive form of income and could help protect against inflation, as one participant expressed, “It’s good to buy stocks because it’s like passive income. You can do your job when it also generates income.” (43Nepalese, M/28 y, W, Degree)

However, some viewed it unfavorably as they thought investment carried substantial risk. In particular, stock investment was akin to gambling, which involved taking chances and the possibility of losing hard-earned money:

You are literally gambling …the price of shares would rise or fall suddenly. …Just in days, you could lose so much. That’s why my aim is to look for a professional job so that I don’t have to depend on unexpected business. (17Nepalese, M/18 y, S, Degree)

Some others held unfavorable views due to their own or their families’ and friends’ negative experiences resulting from poor understanding of financial products:

I bought stock and I sold it. …If I had kept it a bit longer, I could have gotten a much higher return. …I just sold it based on rumors that the stock won’t go up. (27Nepalese, M/20 y, S, Degree)

My uncle didn’t know how to play it. He just went to the bank and was told to invest this and that without any explanation. …In the end, he lost a lot of money. (26Nepalese, F/21 y, S, Degree)

Sometimes, the lack of understanding of financial products could result from language barriers. One participant referred to the Mandatory Provident Fund (MPF), a compulsory pension fund in Hong Kong, as an example:

If you go to work, your employer won’t tell you what’s this or that. They just give you the MPF paper. …People don’t know what’s written there. They just sign it. Which product is better? They don’t know. (2Pakistani, M/22 y, S, SD)

Debt management

Borrowing money as a common practice.

It was quite common for participants to take out loans from different sources, including family, friends, the government, and financial institutions. Some borrowed money from their parents or siblings. Due to close family ties and strong support, paying back the loan was not always expected. Some would also borrow from friends despite feeling uneasy about it:

I had zero income and my wife is jobless. …I felt it was a shame to borrow from my parents. …I asked my brother who is in Qatar. …It’s like a brother thing. He just sent it to me and …no need to return it. (14Pakistani, M/28 y, W, Degree)

To be broke on the 25th of the month but your salary only comes on the 31st. …Those few days you have to live …so I have to borrow from my friend. (38Indian, M/23 y, W, SD)

Some participants who were or had been students took out government student loans for educational expenses:

Hong Kong is so expensive, and so are school fees. I can’t pay it all at once so I had to borrow from the government. (23Filipino, F/23 y, W, Degree)

Some participants borrowed money from banks to buy an apartment. Others borrowed from lending institutions charging high interest rates to pay for tuition fees, buy iPads, or pay off credit card debts. They described their own or their friends’ experiences:

I have a period of time without a job. I have to pay with a credit card every month. I’ve skipped one month …and they started to call me and I was irritated. …Then I realized …why I wouldn’t start to do research tracking the annual rate, and at last, I decided to go to this loan company. (26Nepalese, F/21 y, S, Degree)

My friend found it hard to pay back because the interest rate was high. …She graduated last year and she has only worked for a few months. …She has to pay for the loan and to pay for her credit cards. (22Pakistani, F/25 y, W, S6)

Mostly safe credit card usage with some risks

Approximately one-third of participants owned a credit card. Occasionally, some used their parents’, siblings’, or friends’ credit cards, with approval, when they could not get their own, as illustrated by one participant:

My friend doesn’t use her credit card much. …I just took hers, bought things, and on the same spot transferred money to her account. (22Pakistani, F/25 y, W, S6)

Most credit card users could settle their bills on time, like one who said, “Unlike others who may pay it last minute. I pay it immediately after receiving the statements.” (48Indian, M/24 y, W, Degree)

However, some participants only paid the minimum due on credit cards, especially due to ignorance about interest charges. One did not know the consequences of doing so:

I was studying for an associate degree and I wanted to get as high marks as possible. I thought that if I got into the university then I could pay for them all afterwards, so there’s no pressure if I give minimum payment every month. …I didn’t realize about the interest. I swiped a lot. (26Nepalese, F/21 y, S, Degree)

Perceptions of debt management

Participants considered borrowing money was shameful and could hurt their social relationships. Borrowing money was associated with shame and guilt, especially for people capable of working, instead of borrowing money from others:

We have everything to earn money. … We’re healthy. We have all the physical and mental ability to work. …So we don’t have to depend on other people. (5Indian, M/22 y, S, Degree)

The shame is that …if God has given me a healthy body and I have my hands fine, if I can walk, if I can work, then why go ask someone for anything? (14Pakistani, M/28 y, W, Degree)

Others thought that borrowing money could create tensions with friends or relatives, especially if money was not returned:

Some friends of mine have taken money from me …but they don’t return it. … They’ll say, “I still don’t have money.” …What the bank does is good …charge the interest from them. (52Indian, F/29 y, W, Master’s)

You lend money to relatives or friends …but they might not return it to you. They might not pick up your phones. They might go away from the city. …That’s what I’ve heard….I don’t think they call the police …at the end they are family. (17Nepalese, M/18 y, S, Degree)

Participants considered credit cards to be convenient, and they enjoyed the reward systems. One said, “It’s like an Octopus card Footnote 2 but is more widely used, especially for online shopping. And it’s convenient …you can accumulate points for more savings.” (35Filipino, M/28 y, W, S7) However, many were also aware that credit card use could lead to uncontrollable spending because they could easily overlook how much they had spent:

If I had one, I would go non-stop shopping because I have pressure at work. Who wouldn’t go shopping after work? (38Indian, M/23 y, W, SD)

When you take out your money, you know your limit. Like if your wallet has $5,000 and you’re using it, you’ll notice how much money you’ve left. But credit cards …you’ll keep using it. (8Pakistani, M/23 y, W, SD)

Detecting fraud

Fraud victimization experiences.

Participants were vulnerable to fraud; some shared stories about falling or almost falling for scams or had heard about friends being scammed, relating to possible charity scams, unnecessary lab tests, online gaming, investment fraud, and money lent but not returned. One participant believed he had been scammed when he was asked for a donation on the street:

I’ve been scammed once on the street by a man who’s requesting money for their own institution from their own country. It involves children who are sick. …Because I was young and naive, I didn’t ask them for validation. Although he wanted to scam more money at the time, I didn’t carry too much. (35Filipino, M/28 y, W, S7)

There were unnecessary medical lab tests:

They said they got funding from the government. They did 10 different cancer tests on me for $4000. My mom was very angry about why I did it. She said it’s a scam because I’m so young. I won’t have any cancer right now. (1Indian, F/23 y, S, Master’s)

Money was lost due to an online scam:

There was once an email …saying if you put $10,000 on this account, we’ll give you $20,000 …that kind of scam …but I didn’t do it. …The second time when I bought a computer game online, they just asked me to send some money in advance. …They totally scammed me and then blocked me. (44Nepalese, M/20 y, W, S6)

Friends had also experienced investment fraud:

I have a friend who invested in …some sort of software soccer game. …He saw an advertisement online in a newspaper. He invested and then the money was just gone. …He lost HK$5000. (6Pakistani, M/25 y, W, S6)

They called and encouraged my friend to put in money and said, “…This is very good. You can earn a lot. You can be a rich person. You can do whatever you want to do.” She put a little bit to see. After six months, they kept calling and saying she was doing well, she could do better. And they got everything on that scam and they never called back. (43Nepalese, M/28 y, W, Degree)

Strategies and perceptions for detecting fraud

Participants reported that the flood of suspicious calls and messages they received, and uncertainty about whether they were genuine or not, exposed them to potential scams.

I don’t know if it’s a scam. …I got calls for buying currencies from them. They told me the whole plan, and I’d even go into a discussion and I was close to paying them. I’ve been near that. (49Filipino, M/19 y, W, S6)

Once I borrowed money and after that many financial companies have my number. …They ask what my name is and ask for my information. …I didn’t give them because I know they might want to get my bank information … it’s not safe. (8Pakistani, M/23 y, W, SD)

They also talked about how to avoid scams by understanding their psychology as emotional manipulation to induce feelings of guilt:

They try to confuse you with a lot of situations to guilt trip you. They make you feel bad about other people. They try to trick you into thinking that your life is a lot better than theirs. …They can make you feel good about giving money. … They’re mentally threatening you not in a bad way. …If you have a strong personality, you can fight back easily. But if you’re naive, it can be quite difficult. (35Filipino, M/28 y, W, S7)

Participants reported pretending they did not understand Cantonese (the local spoken language in Hong Kong) or simply ignoring dubious calls or messages as tactics to tackle potential scams:

I think it’s funny because I can speak okay Cantonese. Whenever I get calls from banks or something, I always ask, “Can you speak in English?” and then they just disconnect. (22Pakistani, F/25 y, W, S6)

Some unknown WhatsApp messages are frequent. But I’d ignore them as I know they are dangerous. (30Filipino, F/21 y, W, SD)

This study has various implications. It contributes to conceptual or theoretical understanding, provides insights into practical strategies, and offers directions for further research.

Knowledge or theoretical contributions

Financial decision-making experiences.

This study has contributed knowledge to addressing the research gap by revealing the financial decision-making experiences of younger ethnic minorities in a non-Western context. We examined their behaviors, strategies, and perceptions across a range of financial decisions, including personal budgeting, spending, financial planning, the use of financial products, debt management, and detecting fraud. Many ethnic minority young adults practiced budgeting, using digital tools, parental monitoring, and mental bucketing. They learned about budgeting by observing their parents and gaining hands-on experience with their own earnings. Budgeting was challenging due to limited funds, high living expenses, time demands, stress, and self-control issues. Most were modest spenders, prioritizing basic needs like food and transportation and employing strategies like deferred purchases, bargain shopping, and lump-sum payments. Some opted for instalment payments for expensive items, and when their budget was tight. When spending, they differentiated between needs and wants, sought value for money, worked to meet their spending needs, and purchased for happiness. Saving at least one-third of their monthly income, they utilized external assistance and personal tricks. Their long-term saving goals encompassed education, housing, family, business, retirement, and female autonomy. Financial planning was perceived as an adult responsibility, a safeguard against emergencies, and ensuring peace of mind. They invested in insurance, stocks, cryptocurrency, index funds, forex trading, property, and gold. Parental support, peer mentoring, and self-learning influenced their investment decisions. Lack of knowledge and language barriers may contribute to negative perceptions or experiences of financial products. Informal borrowing from family and friends was commonplace, while others resorted to government or lending institution loans. Around one-third owned a credit card. Most used them safely, but risks exist when using someone else’s card, or they are ignorant about interest charges. They were aware of financial scams and employed preventive strategies like understanding the psychology of scams and ignoring scammers, although they occasionally fell victim to fraud.

Enabling factors to financial decision-making

Factors affecting financial literacy are widely understood in the literature, but less has been examined regarding the factors affecting financial decision-making. Based on the financial decision-making experiences, we further identified various factors or conditions that facilitated ethnic minority young adults’ financial decision-making and enabled them to make better financial decisions. While some other factors acted as barriers, awareness of these barriers and taking action to address them can transform them into enabling factors. The enabling factors include family social capital, intrapersonal characteristics, social dynamics factors, command of knowledge, and facilitative contextual circumstances. These insights can help devise financial literacy education for ethnic minority young adults.

Family social capital. Family social capital enables families to leverage both material and symbolic resources to benefit their members (Furstenberg and Kaplan, 2004 ). In this study family social capital played a crucial role in participants’ financial decision-making, as shown by the resources and support derived from the family relationships, including the passing down of money-related attitudes, norms, and behavior from one generation to another and between siblings. Both intergenerational support and sibling support are key components of this family social capital. In the study, intergenerational support was demonstrated through parental role modeling and involvement. While participants did not mention direct teaching of financial education by parents, parents served as role models from whom their children observed and learned financial attitudes and behaviors. This was how participants acquired their ideas of budgeting. Parents also actively coached financial decision-making by monitoring budgeting, setting spending limits, supervising saving, providing funds to help set up stock accounts, offering joint investment opportunities, and providing financial assistance. Sibling support refers to the emotional and practical support provided by siblings. Study participants sought help from their siblings or provided financial assistance to one another during financial difficulties, as shown by their lending money to each other to avoid unnecessary interest charges that may arise from resorting to other sources. Strong family social capital can be attributed to cultural values emphasizing family relationships, filial piety, and respect for parents.

Intrapersonal characteristics. Intrapersonal characteristics, which comprise personal attributes and life perspectives, are evident in facilitating ethnic minority young adults’ financial decision-making. Personal attributes such as self-motivation, self-discipline, and other competencies play a role. Self-motivation is an inner force that compels behavior (Waitley, 2010 ) and gives people energy to initiate actions and persist in efforts to attain a goal (Robbins and Judge, 2022 ). This study revealed self-motivation to be important in financial decision-making, such as budgeting, as it is difficult for those with lower motivation to sustain a budgeting habit when they consider budgeting as demanding, time consuming, and excessive in work. Self-discipline involves being able to control one’s impulses and desires in favor of long-term goals (American Psychological Association, 2023 ). Participants expressed the importance of self-discipline in successful budgeting, saving, and spending. Math competencies ease financial decisions. Those with numeracy skills tend to feel it easy to engage in budgeting.

Life perspectives are about people’s overall views of life, which include personal philosophies and future orientation and facilitate ethnic minority young adults’ financial decisions. Personal philosophies, which are values and attitudes that can be shaped by personal experiences and family and cultural influences, guide people’s decisions. Spending philosophies can be part of an expression of personal philosophies. Study participants exhibited various personal philosophies, such as simplicity-based, enjoyment-based, and work-to-spend philosophies, reflected in their spending philosophies. Simplicity-based living philosophy emphasizes a minimalist lifestyle over material possessions, as evident in the differentiation between needs and wants in spending philosophy. Enjoyment-based living philosophy values pleasures and living in the present moment, as reflected in the YOLO style of spending philosophy. A work-to-spend philosophy underscores the importance of working hard to support desired spending levels and is shown by the work-as-incentive spending philosophy.

Future orientation is the ability to anticipate future events, give them personal meaning, and operate with them mentally (Nurmi, 1991 ). It is associated with future-oriented behaviors, such as planning and delayed gratification (Strathman et al., 1994 ). This study suggests that individuals with saving goals tend to have a stronger future orientation as they plan for long-term objectives such as education, starting a family, property investments, and retirement. Those who practice delayed gratification by deferring purchases also show future-oriented tendencies.

Social dynamics factors. Social dynamics factors include peer support and vigilance within ethnic communities. Peer support involves ethnic minority young adults helping each other to make financial decisions through monitoring, mentoring, and collaborating to keep track of each other’s financial behaviors towards goals, offer practical advice in planning decisions, and share tips and efforts in decision-making, respectively. For example, peer monitoring serves as a social restraint to help those who struggle with saving. Peer advice is sought concerning investments in their home countries. Collaboration facilitates joint decision-making on buying stocks. Peers, along with parents, also serve as financial socialization agents.

Vigilance in ethnic minority communities is needed to prevent exploitation, as trust is often presumed within these communities. In the study, trust was demonstrated in the common practice of informal borrowing. Although informal loans can be enforced by social or community ties, they are not without risk. Without legal loan agreements, the possibility of bad debts or scams can arise, and did occur within participants’ communities.

Knowledge proficiency. Knowledge proficiency refers to the command of knowledge essential for making informed financial decisions. There are two types of knowledge. The first applies to a range of financial concepts required to navigate choices in everyday financial situations, such as knowledge of effective saving strategies, information on different financial products, and interest charges for instalment plans, loans, and minimum payments on credit cards. It is important, as the study revealed possible risks and negative experiences among participants stemming from a lack of caution or knowledge, like stashing cash away and ignorance of high interest rates when repaying minimum amounts on credit card debts or loans from lending companies. This type of knowledge is also important considering the potential issue of misinformation. Many participants were interested in using financial products such as insurance policies and stocks in Hong Kong and property investment in their home countries. However, their reliance on self-learning through online resources, such as YouTube’s KOL, or listening to peers exposed them to the risks of misinformation. For instance, one participant regretted making a poor decision to sell stocks based on hearsay, lacking proper knowledge of how stocks work.

The second type of knowledge involves protection against fraud. While participants tried to avoid suspicious messages and calls, some fell victim to various scams, accentuating the importance of being equipped with proactive measures, as the ones used, simply ignoring them, appear passive. Familiarizing oneself with common forms of fraud, exercising caution with offers that seem too good to be true, accessing scam alert information, knowing how to report scams, and understanding one’s legal rights when encountering scams would be good anti-fraud measures to learn.

Facilitative contextual circumstances. Various contextual circumstances, including leverage of real-life lessons, access to technology, and language accessibility, can be influential in ethnic minority young adults’ financial decisions. This study shows that the employment and pandemic experiences have been translated into real-life lessons to acquire financial knowledge and attitudes. The first earnings from employment nurtured the ideas of budgeting and financial planning, and provided opportunities for acquiring relevant skills. This suggests that promoting financial planning strategies and saving habits earlier, at least before starting employment, is beneficial. Individuals can avoid making unnecessary mistakes and enter smoothly the world of work that requires many financial decisions. Due to the pandemic, some participants experienced a positive change in their financial attitudes. They realized the importance of preparing for economic uncertainty and were eager to improve their financial planning.

Technology not only provides easy access to online financial materials, but can also facilitate financial decision-making using digital tools, which are particularly useful for following budgeting and tracking expenses. As many participants considered budgeting to be arduous, it can be made easier by adopting technological aids.

Finally, language accessibility can affect the acquisition of financial literacy. Participants’ negative experiences with financial products, such as bank products or MPF, for instance, largely stemmed from a lack of understanding that can also be compounded by a language barrier.

Enriched understanding of financial socialization

Family social capital as an enabling factor of financial decisions aligns with the theory of financial socialization, referring to the process of developing values, attitudes, standards, norms, knowledge, and behaviors promoting financial viability and individual well-being (Danes, 1994 ). Research shows that parents are important socialization agents influencing financial attitudes, such as credit attitudes (Norvilitis et al., 2006 ). Despite arguments suggesting that parental importance declines as children get older (Danes, 1994 ) and peers take on greater influence (John, 1999 ), this study shows that ethnic minority young adults continue to rely heavily on their parents for financial guidance. The findings of this study extend the understanding of financial socialization processes by recognizing the persistent and exceptionally influential roles of ethnic minority parents. Other research also supports that ethnic minority young adults seek parental advice on important education and employment decisions (Chan et al., 2020 ).

Practical implications

The findings on enabling factors and various behaviors shed light on practical suggestions for enhancing financial literacy education, which, in turn, improves the financial decision-making of ethnic minority young adults.

First, in relation to family social capital, if parental influence remains strong in young adulthood, it may be strategically beneficial to involve ethnic minority parents, either as target participants or partners, in tailored financial literacy education.

Second, to promote holistic financial literacy education for ethnic minority young adults, it is necessary to address their specific personal attributes and life perspectives as attitude components, in addition to increasing general financial knowledge and skills. Self-discipline, self-motivation, spending philosophies, and future orientation could be positively fostered by taking into account the unique challenges they face in financial literacy education.

Third, in response to social dynamic factors, while ensuring the accuracy of shared financial information, peer influence can be capitalized on for effective financial literacy education by utilizing collaboration as a learning approach and developing peer mentoring to optimize mutual learning experiences. Also, addressing vigilance in community trust and the potential risks associated with informal loans are important topics to be included in financial literacy education.

Fourth, concerning knowledge proficiency, financial literacy education should incorporate the two essential types of knowledge required for making financial decisions. This includes knowledge about everyday financial situations—such as the use of various financial products, the consequences of different credit card payment options, and the interest rate information associated with formal debts—as well as knowledge about proactive anti-fraud strategies.

Fifth, in response to facilitative contextual circumstances, the current post-pandemic period is an opportune time to offer ethnic minority young adults financial literacy education to capitalize on their increased motivation to learn and improve their financial decisions. Also, as smartphone use is indispensable among young people, including ethnic minorities, user-friendly budgeting apps that fit well their financial situations can help make budgeting efficient, stress-free, and engaging. Moreover, it is important to provide financial literacy education that takes into consideration language needs to ensure a thorough grasp of financial concepts. For instance, interpretation support can ease language barriers. However, in the long run, policy interventions in the educational system, such as remedial language support and learning Chinese at a young age, are necessary.

Last, various financial behaviors prone to mental accounting bias should be addressed in financial literacy education to equip ethnic minority young adults with the skills to make optimal decisions. Mental accounting refers to the cognitive operations to organize, create mental labels, and keep track of money or financial activities (Thaler, 1985 ). An important concept in this theory is that money is fungible or interchangeable, regardless of its source or purpose. However, people often violate this principle and see money differently, resulting in suboptimal decisions. For instance, some participants might treat credit cards as different mental accounts. They were willing to spend more on credit cards compared to cash, as there seems to be no loss at the time of purchase and the payment can be deferred. This could lead to overspending. Also, in saving, some might stash cash away under the mattress, ignoring the interest earnings from a savings account. Other possible behaviors could be funding a low-interest savings account while carrying high-interest debt.

Research implications

Further research can address several issues or limitations. First, since ethnic Chinese young adults did not participate in this study, their inclusion in future research would facilitate a more comprehensive investigation. As young adults, they likely share similarities, such as spending philosophies, self-learning about financial information, and exposure to potential scams. However, differences may also exist in certain areas. The use of external restraint and personal hacks in financial matters, such as strong roles of parents and boyfriends in monitoring spending, and stashing cash away was less commonly observed among Chinese young adults. Informal borrowing and property investment in their home country were also unique characteristics of ethnic minority young adults. Further research can confirm these possibilities.

Second, this study may not fully reflect the situation of ethnic minority young adults with a lower socio-economic status. This is because the sample was generally well educated, as half of the participants had an undergraduate degree, and almost one-fifth educated to secondary education. In addition, the interviews were conducted in English, indicating a good command of the language among the participants. Future research may include those who are less socioeconomically advantaged as they may have different mechanisms surrounding financial decision-making.

Third, the relative influence of parents and peers across life stages, as well as differences in intrapersonal characteristics as facilitating factors between ethnic minority and non-ethnic minority young people, can be investigated to offer insights into tailored financial literacy education.

Moreover, we acknowledge the limitation of small sample size in a qualitative study, which aims to reveal themes for the financial literacy of ethnic minority young adults, an underexamined group. A future quantitative study for a larger population will be needed to allow for a broader generalization.

This study examined financial decision-making among ethnic minority young adults in Hong Kong and identified major enabling factors for their financial decision-making. Ethnic minority young adults employed strategies for budgeting, but they also found budgeting challenging. They had various spending philosophies, while basic needs were mostly a priority. Saving at least one-third of their income was common, and they had long-term financial planning goals. They used financial products both in Hong Kong and their home countries. Informal borrowing was common, despite some turning to other sources of loans. One-third used credit cards, incurring occasional risks. They were aware of scams and employed protective tactics, but still fell victim to scams.

Enabling factors to financial decisions included family social capital, intrapersonal characteristics, social dynamics factors, knowledge proficiency, and facilitative contextual circumstances. To enhance financial decision-making among ethnic minority young adults, the following can be considered. First, leveraging parental influence by involving them in financial education efforts. Second, fostering positive financial attitudes alongside increasing financial knowledge for a holistic financial education. Third, optimizing peer influence through collaborative learning and peer mentoring and raising awareness about community trust and potential issues with informal borrowing. Fourth, covering financial concepts for everyday financial decision-making and mental accounting bias, as well as practical knowledge for fraud prevention. Fifth, capitalizing on pre-employment and post-pandemic periods for timely financial education. Sixth, developing tailored digital tools and language support for specific ethnic communities. Finally, conducting further research is necessary. This includes the inclusion of ethnic Chinese and ethnic minority young people from various socioeconomic backgrounds and investigating the relative importance of parental and peer influence across different age groups. Moreover, comparing the intrapersonal characteristics as facilitating factors between ethnic minority and non-ethnic minority young people and expanding studies to include a larger population to enable generalization are also important.

Data availability

The data analysed during this study are not publicly available to protect research participant privacy but are available from the corresponding author upon reasonable request.

Information in parentheses denotes participant ID with ethnicity, gender and age, status as studying or working (S or W), and educational level, respectively. Education level may refer to the level of secondary school (e.g., S6), sub-degree (SD), Bachelor’s degree (Degree), or Master’s degree (Master’s).

An Octopus card is a rechargeable card that can be used on public transport and in convenience stores in Hong Kong.

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Acknowledgements

The research project was funded by the Investor and Financial Education Council in Hong Kong while the author was working at Hong Kong Baptist University. The research was conducted at that time. This paper was supported by the Children and Youth Research Centre and an Institutional Development Grant of Saint Francis University, Hong Kong, following the author’s transition to the new position at that university.

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Cho, E.YN. A qualitative investigation of financial decision-making and enabling factors among ethnic minority young adults in Hong Kong. Humanit Soc Sci Commun 11 , 1113 (2024). https://doi.org/10.1057/s41599-024-03605-1

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DOI : https://doi.org/10.1057/s41599-024-03605-1

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