In a true experiment, participants are randomly assigned to different study conditions. A quasi-experiment lacks this random assignment.
True experiments are also usually conducted in controlled laboratory settings, which facilitates control of confounding variables that may impact study results. Quasi-experimental designs often collect data in real-world settings, which increases external validity but reduces control of confounds.
Finally, both true experiments and quasi-experiments generally involve the manipulation of an independent variable to determine its causal effect on a dependent variable. However, in a quasi-experimental study, researchers may have less control over this manipulation (e.g., they may be studying the impact of an intervention or treatment that has already happened).
Continue reading: What is the difference between a true experiment and a quasi-experiment?
Practical or ethical concerns may prevent researchers from using a true experimental design:
Practical concerns that prevent researchers from conducting a true experiment may include the cost of a study or the time required to design the experiment and collect and analyze data.
Ethical concerns may also limit the feasibility of true experimental research. It would be unethical to intentionally prevent study participants from accessing medication or other treatments that the researcher knows would benefit them.
In these cases, a quasi-experimental design may be more appropriate.
Continue reading: When should I use quasi-experimental design?
The four main types of mixed methods research designs differ in when the quantitative and qualitative data are collected and analyzed:
- Convergent parallel design: Qualitative and quantitative data are collected and analyzed simultaneously and analyzed separately. The results are then compared, and similarities and dissimilarities between the findings are discussed.
- Embedded design: Either qualitative methods or quantitative methods are the focus of a study, and additional methods are used to support these main findings.
- Explanatory sequential design: Quantitative data are first collected and analyzed, then the results or patterns are explained using qualitative methods.
- Exploratory sequential design: Qualitative data are first collected and analyzed, then emergent themes are explored in more detail using quantitative methods.
Continue reading: What are the types of mixed methods research designs?
Mixed methods research questions combine qualitative methods and quantitative methods to answer a research question. Examples of mixed methods research questions include the following:
- How does the proximity of student housing to college campuses (quantitative) relate to student perceptions of school culture (qualitative)?
- To what extent do air pollution levels (quantitative) correspond to residents’ perception of air quality (qualitative) in urban areas?
- What barriers do community members describe to accessing mental health services (qualitative), and how do these barriers correlate with use of these services within this community (quantitative)?
Continue reading: What are some mixed methods research questions?
Data collection is the process of gathering data (measurements, observations, and other information) to answer a research question. Though many different methods of data collection exist, all are systemic and follow a procedure defined before data collection begins. Data can be qualitative or quantitative.
Continue reading: What is data collection?
Operationalization is when you define how a variable will be measured. Operationalization is especially important in fields like psychology that involve the study of more abstract ideas (e.g., “fear”).
Because fear is a construct that cannot be directly measured, a researcher must define how they will represent it. For example, in studies involving mice, fear is often operationalized as “”how often a mouse freezes (i.e., stops moving) during an experiment.”
Operationalization can be used to turn an abstract concept into a numerical form for use in quantitative research.
Some operationalizations are better than others. It is important to consider both reliability and validity (how consistent and accurate a measurement is, respectively) when operationalizing a construct.
Continue reading: What is operationalization?
Qualitative and quantitative research methods are used to investigate different types of research questions.
Quantitative methods are best if:
- The topic you’re studying can easily be expressed using numbers
- You want to use statistics to test a hypothesis
Qualitative methods are best if:
- The topic you’re studying cannot be expressed numerically
- You’re studying a new research area with limited existing theories
- You want to gain deep insight into a nuanced issue
Continue reading: How do I decide between qualitative and quantitative research?
There are many types of qualitative research. The following are five common approaches:
- Ethnography: the researcher immerses themself in a group to understand its culture
- Grounded theory: data are collected to create new theories that may guide additional data collection
- Phenomenology: the experiences of individuals are used to study a phenomenon
- Narrative research: the way stories are told is studied to understand human experiences
- Action research: a problem is studied and solutions are generated simultaneously
Choosing the right approach depends on the research question you are studying.
Continue reading: What are the types of qualitative research?
Member checking is when participants are allowed to review their data or results to confirm accuracy. This process can happen during or after data collection.
In qualitative research, data are often collected through interviews or observations. Allowing a participant to review their data can help build trust and ensure that their thoughts and experiences are being accurately expressed.
Continue reading: What is member checking in qualitative research?
Qualitative data are generally narrative in nature. They may include interview transcripts or experimenter observations. Different approaches exist to analyze qualitative data, but common steps are as follows:
- Organize the data
- Code the data, sorting it into relevant categories
- Extract and interpret key ideas
Common qualitative data analysis techniques include content analysis, thematic analysis, and discourse analysis.
Continue reading: How do I analyze qualitative data?
