What are some types of inductive reasoning?

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.

Frequently asked questions: Methodology

What is differential attrition?: 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.
What’s the difference between action research and a case study?: 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.
What is the main purpose of action research?: 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.
How is action research used in education?: 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.
What is a cycle of inquiry?: 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.”
How do you make quantitative observations?: 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.
What is the difference between criterion validity and construct validity?: 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.
Why are convergent and discriminant validity often evaluated together?: 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.
Why are convergent and discriminant validity often evaluated together?: 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

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
Why is content validity important?: 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.
In what ways are content and face validity similar?: 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.
Is snowball sampling random?: 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.
Is snowball sampling quantitative or qualitative?: 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.
Is snowball sampling biased?: 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.
When would it be appropriate to use a snowball sampling technique?: 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)
How can you ensure reproducibility and replicability?: The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.
What’s the difference between reproducibility and replicability?: 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.
Why are reproducibility and replicability important?: Reproducibility and replicability are related terms.

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.
What is the difference between quota sampling and stratified sampling?: 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).
What is the difference between purposive sampling and convenience 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.
What is the difference between random sampling and convenience sampling?: 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.
What is the difference between quota sampling and convenience sampling?: 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.
What is a sampling frame?: 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.
What is the difference between stratified and cluster sampling?: 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.
Is a systematic review primary research?: A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
What is the difference between an observational study and an experiment?: 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.
How do you define an observational study?: 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.
Who should assess face validity?: 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.
Why is face validity important?: 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.
What is the definition of face validity?: 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.
How do I measure construct validity?: 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.
Why does construct validity matter?: 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.
What is the definition of 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
What are the pros and cons of naturalistic observation?: 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.
What is the definition of a naturalistic observation?: 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.
What’s the definition of a dependent variable?: 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)
What’s the definition of an independent variable?: 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).
How do you write focus group questions?: 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
When should you use a structured interview?: 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.
What is social desirability bias?: 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.
What is an interviewer effect?: 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.
When should you use a semi-structured interview?: 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.
When should you use an unstructured interview?: 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.
What are the 4 main types of interviews?: 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.
How do you use deductive reasoning in research?: 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.
What is deductive reasoning?: 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.
What’s the difference between inductive and deductive reasoning?: 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.
How is inductive reasoning used in research?: 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.
What is inductive reasoning?: 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.
What is a hypothesis?: 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).
What are the pros and cons of triangulation?: 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.
What are the types of triangulation?: 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
What types of documents are usually peer-reviewed?: 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.
Why is peer review important?: 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.
How does the peer review process work?: In general, the peer review process follows the following steps:

First, the author submits the manuscript to the editor.

The editor can either:

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.
When should I use exploratory research?: 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.
What is exploratory research?: 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.
When should I use explanatory research?: 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.
What’s the difference between exploratory and explanatory 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.
What is explanatory research?: 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.
What’s the difference between clean and dirty data?: 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.
When you do you clean data?: 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.
How do you clean data?: 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.
Why does data cleaning matter?: 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.
What is data cleaning?: 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.
What is research misconduct?: 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.
What’s the difference between anonymity and confidentiality?: 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.
Why do research ethics matter?: 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.
What are ethical considerations in research?: 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.
Is multistage sampling a probability sampling method?: 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.
What are the pros and cons of multistage sampling?: 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.
What are the main types of mixed methods research designs?: 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.
What is triangulation in research?: 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.
What is multistage sampling?: 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.
Is the correlation coefficient the same as the slope of the line?: 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.
What do the sign and value of the correlation coefficient tell you?: 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.
What are the assumptions of the Pearson correlation coefficient?: 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
What are the main types of research design?: 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.
Why is research design important?: 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.
What do I need to include in my research design?: 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)
What is a research design?: 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.
How do you administer questionnaires?: 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.
How do you order a questionnaire?: 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.
What’s the difference between closed-ended and open-ended questions?: 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.
What’s the difference between questionnaires and surveys?: 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.
Why doesn’t correlation imply causation?: 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.
What’s the difference between correlation and causation?: 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.
What’s the difference between correlational and experimental research?: 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.
How many variables are in a correlation?: A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
What is a correlation coefficient?: 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.
What is correlational research?: 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.
What is a correlation?: 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.
How do you avoid measurement errors?: 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.
Is random error or systematic error worse?: 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.
What’s the difference between random and systematic error?: 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).
How do you plot explanatory and response variables on a graph?: 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.
How do explanatory variables differ from independent variables?: 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.
What are explanatory and response variables?: 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.
What are the requirements for a controlled experiment?: 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.
What are the types of extraneous 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.
What’s the difference between extraneous and confounding variables?: 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.
What is a factorial design?: 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.
What are the pros and cons of a within-subjects design?: 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
What are the pros and cons of a between-subjects design?: 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.

Advantages:

Prevents carryover effects of learning and fatigue.

Shorter study duration.

Disadvantages:

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.
Can you use a between- and within-subjects design in the same study?: 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.
What’s the difference between within-subjects and between-subjects designs?: 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.
When do you use random assignment?: 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.
How do you randomly assign participants to groups?: 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.
What’s the difference between random assignment and random selection?: 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.
What is random assignment?: 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.
What does “controlling for a variable” mean?: “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.
Why are control variables important?: 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.
What is a control 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.
Why should you include mediators and moderators in a study?: 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.
How can you tell if something is a mediator?: 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.
What’s the difference between a confounder and a mediator?: 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.
What’s the difference between a mediator and a moderator?: A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
How do I perform systematic sampling?: 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 kth member of the population as your sample.
What is systematic sampling?: 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.
Can I stratify by multiple characteristics at once?: 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.
When should I use stratified sampling?: 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.
What is stratified sampling?: 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.
What are some advantages and disadvantages of cluster sampling?: 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.
What are the types of cluster sampling?: 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.
What is cluster sampling?: 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.
When should I use simple random sampling?: 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.
What is an example of 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.
What is simple random sampling?: 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.
When should I use a quasi-experimental design?: 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.
What is a quasi-experiment?: 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.
Why is blinding important in research?: 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.
What is the difference between single-blind, double-blind and triple-blind studies?: 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.
What is blinding in research?: Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment.
Do experiments always need a control group?: 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.
What is the difference between a control group and an experimental group?: 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.
Are Likert scales ordinal or interval scales?: 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.
What is a Likert scale?: 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.
What’s the difference between concepts, variables, and indicators?: 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.
How do you analyze qualitative data?: 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.
What are the main qualitative research approaches?: 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.
What is hypothesis testing?: 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.
What is operationalization?: 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.
What are the benefits of collecting data?: 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.
What is data collection?: 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.
How do I prevent confounding variables from interfering with my research?: 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.
What is the difference between confounding variables, independent variables and dependent 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.
Why do confounding variables matter for my research?: 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.
Can I include more than one independent or dependent variable in a study?: 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.
Can a variable be both independent and dependent?: 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!
What is an example of an independent and a dependent variable?: 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.
Why are independent and dependent variables important?: 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.
What is non-probability sampling?: 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.
What is probability 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.
How do you avoid sampling bias?: Using careful research design and sampling procedures can help you avoid sampling bias. Oversampling can be used to correct undercoverage bias.
What are some types of sampling 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.
Why is sampling bias important?: Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.
What is sampling error?: A sampling error is the difference between a population parameter and a sample statistic.
What’s the difference between a statistic and a parameter?: A statistic refers to measures about the sample, while a parameter refers to measures about the population.
When are populations used in research?: 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.
Why are samples used in research?: Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
What are threats to external validity?: There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment and situation effect.
What are the two types of external validity?: 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).
What is external validity?: The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.
What are the disadvantages of a cross-sectional study?: 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.
Why do a cross-sectional 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.
How long is a longitudinal study?: Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
What is an example of a longitudinal study?: 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.
What are the pros and cons 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.
What is the difference between a longitudinal study and a cross-sectional study?: 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.

Longitudinal study Cross-sectional study

Repeated observations Observations at a single point in time

Observes the same group multiple times Observes different groups (a “cross-section”) in the population

Follows changes in participants over time Provides snapshot of society at a given point
What are threats to internal validity?: There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction and attrition.
What is internal validity?: 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.
What is mixed methods research?: In mixed methods research, you use both qualitative and quantitative data collection and analysis methods to answer your research question.
How do I decide which research methods to use?: 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.
What is a confounding variable?: 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.
What is the difference between discrete and continuous variables?: 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).
What is the difference between quantitative and categorical variables?: 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.
What are independent and dependent variables?: 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.
What is 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.
What is the difference between internal and external validity?: Internal 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.
What’s the difference between reliability and validity?: 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.
What is sampling?: 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.
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.
What’s the difference between method and methodology?: 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.

Longitudinal study	Cross-sectional study
Repeated observations	Observations at a single point in time
Observes the same group multiple times	Observes different groups (a “cross-section”) in the population
Follows changes in participants over time	Provides snapshot of society at a given point

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