Posted November 2019 by Clifton Stamp, B.S. Psychology; M.A. Rehabilitation Counseling, M.A. English; 10 updates since. Reading time: 5 min. Reading level: Grade 11+. Questions on designing psychology experiments? Email Toni at: firstname.lastname@example.org.
Experimental psychology uses scientific investigation of essential psychological processes in humans and animals to test and refine hypotheses. Hypotheses are “educated guesses” about the nature of behavior or thought processes that need to be tested to determine if they’re sound. Testing hypotheses is in the realm of experimentation. Designing psychology experiments that return useful, consistent and reliable results is critical to researcher’s ability to establish cause-and-effect relationships in the many variables that influence behavior.
All psychology experiments seek to describe, explain and predict behaviors or mental processes. To be able to predict and make inferences about behaviors requires us to have a good understanding of how variables act on each other. All experimentation in psychology is guided by the scientific method, a set of widely agreed upon principles and procedures that governs all experimentation in the sciences.
Steps to Conduct an Experiment in Psychology
The first step in designing a psychology experiment is to define a research question. The research question helps formulate the hypothesis. A research question is based on observations about a phenomenon in psychology. An exhaustive search of all professional literature on the topic helps refine and define the research question. This is called a literature review and can be lengthy. Researchers look at academic journals and books.
Next, after a thorough review of the literature, researchers form a hypothesis for testing. A good hypothesis must be quantifiable; that is, it can be measured. It must also be falsifiable. A falsifiable hypothesis can be disproven if it is untrue.
Choosing sample groups is the third step in the process and involves randomly choosing a large enough group of test subjects that the population is represented well by the sampled group. The overall group is then broken down into a control group and an experimental group.
Testing the hypothesis is the fourth step. It involves either descriptive or experimental research. Descriptive research methods include correlation studies, surveys, naturalistic observation and case studies. Correlation studies are common descriptive kinds of psychological research. Correlation studies don’t allow researchers to identify a cause-and-effect relationship between variables, but they’re excellent at uncovering possible relationships.
Experimental research examines possible cause-and-effect relationships between variables. Experimental research involves careful manipulation of one variable (the independent variable) and measuring changes in another variable (the dependent variable). The most simple experimental design uses a control group and an experimental group. The experimental group experiences whatever treatment or condition that’s under investigation while the control group does not.
After an experiment is complete, researchers analyze their results and see what conclusions can be drawn. This is usually a statistics-heavy phase of experimentation. Statistics can be used to describe outcomes and also make inferences from research outcomes.
Tips For Designing Psychology Experiments
The goal of experimentation in psychology is to discover cause-and-effect relationships between variables. Doing so requires experiments to be narrowly structured to eliminate bias and confounding variables. There are also some differences between qualitative and quantitative research designs. Essentially, quantitative research produces results that are numerical and can be treated mathematically. Qualitative research generates non-numerical results, often in the form of interviews, questionnaires and surveys.
Here are a few tips to design the most effective quantitative psychology experiments:
- Clarify your research question and streamline your hypothesis. You must have a clear idea of what you’re testing and what your goals are. The more variables you include, the harder it is to control for confounding variables and the less able you are to make statements about the nature of the relationships between variables. Most experimental studies test one independent variable’s effects on one dependent variable.
- Carry out thorough operationalization of your measures and define your variables. For example, if you’re looking at “job satisfaction,” how is that concept going to be measured in your experiment? Operationalization ensures that your variables can be quantified, by taking a “big picture” concept and defining it more narrowly so that it can be measured objectively. A design that attempts to show increased job satisfaction after bonuses are increased would require a standardized, industry-recognized metric for rating job satisfaction. If such a measure doesn’t exist, it’s a good idea to re-think how your variables are going to be measured. You have to have a clearly defined and measurable independent variable and dependent variable.
- Regardless of your experiment’s overall design, you must control for all confounding variables. Confounding variables are unintended conditions that reduce or eliminate your ability to draw inferences about your independent variable’s effects on your dependent variable. Confounding variables ruin experiments so thoroughly that taking scrupulous care in the design phase of an experiment to control for them is essential.
- Eliminate or control for researcher bias. Bias is the potential for a researcher to influence the outcomes of an experiment. In quantitative research, bias can be eliminated. Bias is more of a problem in qualitative research, in which it cannot be entirely eliminated.
- Ensure that your control and experimental groups are thoroughly randomized. To be able to extrapolate your results, your control group and experimental groups must be as similar as possible.
Designing sound experiments requires a precise, exacting clarity on the part of researchers when it comes to what they’re wanting to measure and how to go about that. When an experiment in psychology is correctly designed, it can be replicated by other researchers and yield similar results. Replicability is important, especially when considering the publication of a research experiment in a professional journal. The more one’s experiment can be conducted by others, with results that support the original hypothesis, the stronger that hypothesis grows over time.
Theories are composed of hypotheses that have met numerous tests, including replicability. Good research design ensures that theories in psychology are supported by rigorous testing standards.
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