Independent Measures
Participants are split into different groups, with each group trying out a different version of the experiment. For example, one group might receive a new teaching method, while another group receives the traditional method.
| Strengths | Weaknesses |
| – Eliminates order effects: Since participants are only exposed to one condition, order effects (such as practice or fatigue) are minimized – Reduces demand characteristics: Participants are less likely to guess the purpose of the study or change their behavior based on previous experiences – Suitable for large sample sizes: It’s relatively easy to recruit a large number of participants for each condition | – Requires more participants: Recruiting a large sample can be time-consuming and costly – Individual differences: Variability between participants may confound results, making it challenging to determine the true effect of the independent variable |
Matched Pairs
In this design, participants are first matched into pairs based on relevant characteristics (e.g., age, gender, IQ), and then each member of the pair is randomly assigned to a different experimental condition.
| Strengths | Weaknesses |
| – Controls for individual differences: By matching participants based on relevant variables, individual differences are minimized, enhancing the internal validity of the study – Reduces participant variability: Since participants are matched based on relevant characteristics, variability between conditions is reduced, making it easier to detect the effect of the IV – Requires fewer participants than independent measures: Matching reduces the need for a large sample size compared to independent measures designs | – Matching process can be time-consuming: Identifying and matching participants based on relevant characteristics can be labor-intensive – Limited: The effectiveness of matching depends on the variables selected for matching, and not all relevant variables may be matched |
Quasi Experimental Design
In this design, researchers investigate the effect of an independent variable. Quasi-experimental designs are often used when random assignment is impractical or unethical.
| Strengths | Weaknesses |
| – Practical and ethical considerations: Quasi-experimental designs are useful when random assignment is not feasible due to ethical concerns or practical constraints – Real-world applicability: Often reflect real-world conditions more closely than experimental designs, increasing the ecological validity of the findings – Allows for studying naturally occurring phenomena: Especially traits that cannot be manipulated experimentally, such as the effect of natural disasters or socioeconomic factors on behavior | – Limited internal validity: Without random assignment, it’s difficult to establish causal relationships between the independent and dependent variables, reducing the internal validity of the study – Threats to validity: Quasi-experimental designs are susceptible to various threats to validity, such as selection bias, history, and maturation, which may confound the results |
Repeated Measures
In this design, the same participants are tested in all the various conditions of the experiment, hence serving as their own control. For example, you’re investigating whether a new teaching method will lead to memory retention. The same participant undergoes a traditional teaching method, then the new teaching method, and you compare how well they remember with each method, This way, each student serves as their own control, and you can be more confident that any differences you see are because of the methods themselves, not just because of differences between the students.
| Strengths | Weaknesses |
| – Control for individual differences: Since the same participants are used across conditions, individual differences that could affect the results (like age, gender, or personality) are controlled for, making the comparisons more accurate – Greater statistical power: Repeated measures designs often require fewer participants compared to independent measures designs, which can lead to greater statistical power and sensitivity to detect differences – Efficient use of participants: Using the same participants for all conditions saves time and resources compared to recruiting separate groups for each condition | – Order effects: Participants’ performance in later conditions may be influenced by their experiences in earlier conditions. For example, they might improve simply because they’re getting more practice – Carryover effects: The effects of one condition may carry over and affect performance in subsequent conditions – Sensitization: Participants may become aware of the purpose of the study or the manipulation, affecting their behavior in subsequent conditions – Counterbalancing required: To counteract order effects, researchers often need to counterbalance the order in which conditions are presented, adding complexity to the experiment |
Detailed Notes on Research Methods