A case study is a detailed, in-depth exploration of a subject. This subject, or “case,” could be just about anything: an individual, a group, a historical event, and so on.
When writing up your case study, QuillBot’s Paraphraser can help you find just the right words and phrasing.
Continue reading: Case Study | Definition, Examples & Steps
A quasi-experimental design is used to establish a cause-and-effect relationship between independent and dependent variables. However, unlike in a true experiment, participants are not randomly assigned to experiment conditions.
Quasi-experimental designs still involve the manipulation of an independent variable but may lack control over extraneous variables that could impact a study’s outcomes.
| Characteristic | Experimental design | Quasi-experimental design |
|---|---|---|
| Random assignment of participants | ||
| Manipulation of an independent variable | ||
| Control of extraneous variables | ? |
Continue reading: Quasi-Experimental Design | Definition, Types & Examples
Qualitative and quantitative research use different methods to explore research questions. Qualitative research involves non-numerical data, such as interviews or participant observation. Quantitative research relies on numerical data gathered through surveys or experiments.
| Property | Qualitative Research | Quantitative Research |
|---|---|---|
| Focus | Gaining deep insight into a new or nuanced topic | Testing hypotheses, exploring cause-and-effect relationships, exploring patterns |
| Type of data | Narrative (e.g., text, pictures, audio and video recordings) | Numerical |
| Data collection methods | Interviews, participant observation, focus groups, collection of documents | Questionnaires, surveys, experiments |
| Study location | Can take place in naturalistic settings | Generally takes place in controlled settings |
| Sample size | Small, targeted sample | Large, random sample |
| Data analysis | Thematic analysis, coding | Statistical analysis |
| Strengths | High validity | High reliability and generalizability |
| Weaknesses | Time-consuming, lacks generalizability, subjective | Cannot study phenomena that cannot be expressed numerically, lacks validity |
Continue reading: Qualitative vs Quantitative Research | Methods & Differences
Mixed methods research combines qualitative methods, which use nonnumerical data like text, transcripts, and images, with quantitative methods, which use numerical data.
A common misconception is that mixed methods studies simply include both qualitative and quantitative data. However, when adopting a mixed methods approach, you must carefully consider when and how to collect and integrate these different data sources. Mixed methods research can therefore be time consuming, but it offers the benefit of providing a more holistic answer to your research question.
Continue reading: Mixed Methods Research | Types & Examples
Quantitative research uses numerical data, such as measurements, survey responses, and experiment results, to explore a research question. Quantitative data are analyzed with statistics to test a theory or hypothesis.
Quantitative research methods can be applied to find patterns, identify causal relationships, and model real-world processes. Many fields, including biology, chemistry, economics, physics, and psychology, use quantitative methods.
Not all phenomena can be expressed using numbers—in such cases, qualitative research methods should be used instead. Qualitative and quantitative methods can also be combined in mixed methods research.
Continue reading: Quantitative Research | Definition, Methods & Examples
Qualitative research uses narrative, nonnumerical data to explore research questions. Nonnumerical data may include text, photos, or videos. Qualitative research is useful for gaining deep insight into a topic or generating new ideas and theories.
Qualitative research can be conducted on its own or in combination with quantitative research methods (which use numerical data). The combination of qualitative and quantitative approaches is called mixed methods research.
Continue reading: Qualitative Research | Definition, Methods & Examples
When conducting research, especially research that involves human participants, it’s important to adhere to research ethics. Research ethics are principles that provide a framework for researchers to distinguish “right” from “wrong.” They guide scientists throughout the research process to maximize the benefits of their work while minimizing the potential for harm.
Research ethics principles vary between countries and organizations but share common goals:
Continue reading: Research Ethics | Definition, Principles & Examples
A conceptual framework identifies different variables in a study and illustrates the relationship between them.
Variables are quantities, traits, or conditions that can take on different values. An experiment tests the cause-and-effect relationship between an independent variable and a dependent variable, but it may also contain control variables, mediator variables, moderator variables, and confounding variables.
Continue reading: What Is a Conceptual Framework? | Examples & Tips
In an experiment, a researcher tests a hypothesis by manipulating an independent variable and measuring its impact on a dependent variable. A variable is any property that can take on different values (e.g., height, temperature, GPA).
Experiments test cause-and-effect relationships:
In other words, you systematically vary the independent variable and measure the resulting changes in the dependent variable.
| Independent variable | Dependent variable |
|---|---|
| Manipulated by the researcher | Measured by the researcher |
| Acts as the cause | Represents the effect |
| The “if” part of a hypothesis (i.e., “if I change [this variable]…”) | The “then” part of a hypothesis (i.e., “… then this variable should change.”) |
| Plotted on the x-axis of a graph | Plotted on the y-axis of a graph |
| Occurs earlier in time in an experiment | Occurs later in time in an experiment |
| Also called an input, predictor variable, explanatory variable, manipulated variable, or treatment variable | Also called an output, predicted variable, explained variable, measured variable, or outcome |
Continue reading: Independent and Dependent Variables | Difference & Examples
When choosing how to measure something, you must ensure that your method is both reliable and valid. Reliability concerns how consistent a test is, and validity (or test validity) concerns its accuracy.
Reliability and validity are especially important in research areas like psychology that study constructs. A construct is a variable that cannot be directly measured, such as happiness or anxiety.
Researchers must carefully operationalize, or define how they will measure, constructs and design instruments to properly capture them. Ensuring the reliability and validity of these instruments is a necessary component of meaningful and reproducible research.
| Reliability | Validity | |
|---|---|---|
| Definition | Whether a test yields the same results when repeated. | How well a test actually measures what it’s supposed to. |
| Key question | Is this measurement consistent? | Is this measurement accurate? |
| Relationship | A test can be reliable but not valid; you might get consistent results but be measuring the wrong thing. | A valid test must be reliable; if you are measuring something accurately, your results should be consistent. |
| Example of failure | A bathroom scale produces a different result each time you step on it, even though your weight hasn’t changed. The scale is not reliable or valid. | A bathroom scale gives consistent readings (it’s reliable) but all measurements are off by 5 pounds (it’s not valid). |
Continue reading: Reliability vs Validity | Examples and Differences
