Research Methods in Psychology
Experimental Research (Field Experiment)
IGNOU|MAPC 1st Year|MPC 005|Block 2, Unit 7
Brijesh Chaturvedi
B.Tech in CSE & Masters in Psychology
COMPARISON
Laboratory vs Field Experiments
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| Aspect | Laboratory Experiment | Field Experiment |
|---|---|---|
| Setting | Artificial, highly controlled | Natural, real-world setting |
| Control | High control over extraneous variables | Lower control over extraneous variables |
| Manipulation | IV is precisely manipulated | IV is manipulated but within natural limits |
| Groups | Random assignment is easier | Often uses existing intact groups |
| Purpose | Testing theories under pure conditions | Testing theories in practical situations |
| Relevance | High internal validity, low external validity | High external validity (generalizability) |
EVALUATION
Strengths & Weaknesses of Field Experiments
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Strengths
- Greater ecological validity and realism.
- Results are easier to generalize to real-world settings.
- Allows study of complex social interactions that cannot be simulated in a lab.
Weaknesses
- Difficult to strictly control extraneous variables.
- Ethical challenges (e.g., lack of informed consent in public settings).
- Higher cost and time required to organize in the field.
METHODOLOGY
6 Steps in Constructing Field Experiments
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1. Planning: Defining the problem, literature review, and formulating hypotheses.
2. Sampling: Identifying the population and selecting a representative sample in the natural setting.
3. Experimental Design: Choosing the blueprint (e.g., pre-test post-test control group) adapted for the field.
4. Tools Selection: Creating or choosing reliable instruments to measure the dependent variable.
5. Procedure / Data Collection: Administering the treatment and recording observations accurately.
6. Data Analysis: Using appropriate statistical tests to evaluate the hypothesis.
VARIANCE CONTROL
Kerlinger's 3 Principles of Variance Control (MAXMINCON)
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1. Maximal Systematic Variance
Ensure the IV manipulation is strong enough to clearly separate the experimental groups and produce an observable effect on the DV.
2. Control Extraneous Variance
Minimize the influence of unwanted outside variables by randomization, matching, or building them into the design as an additional IV.
3. Minimal Error Variance
Reduce random fluctuations by using highly reliable measurement tools and maintaining standardized experimental conditions.
DESIGN CRITERIA
6 Criteria of a Good Experimental Design & Overview
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1. Adequate control of variables: High internal validity.
2. Lack of artificiality: Ensures external validity and generalizability.
3. Basis for comparison: Use of a control group to evaluate the experimental effect.
4. Adequate information from data: Sufficient sample size and statistical power.
5. Unconfounded data: No hidden variables driving the measured effect.
6. Representativeness: The sample truly reflects the population under study.
CONNECT & LEARN
Thank You for Learning with Us!
07
Brijesh Chaturvedi
B.Tech in CSE and Masters in Psychology
Host & Educator • Slice of Mind
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