- Independent groups design
- Different participants are assigned to each condition. Advantages: no order effects (participants only experience one condition). Disadvantages: participant variables may differ between groups, requiring more participants and random allocation to minimise pre-existing differences. Also called between-subjects design.
- Repeated measures design
- The same participants complete all conditions, serving as their own control. Advantages: participant variables are controlled (differences between conditions cannot be due to pre-existing participant differences); requires fewer participants. Disadvantages: order effects (practice, fatigue, boredom) and heightened demand characteristics (participants more easily guess the study's purpose across conditions). Also called within-subjects design.
- Matched pairs design
- Participants are paired on key variables likely to affect the DV (e.g. IQ, age, baseline performance), then one member of each pair is assigned to each condition. Combines reduced participant variability (like repeated measures) with absence of order effects (like independent groups). The main limitation is the practical difficulty of finding well-matched pairs, especially on multiple variables simultaneously.
- Order effects
- In repeated measures designs, performance in a later condition may be affected by experience from an earlier one. Practice effects improve performance with task familiarity; fatigue and boredom effects impair later performance. Order effects do not exist in independent groups designs because each participant experiences only one condition.
- Counterbalancing
- The standard solution to order effects in repeated measures designs. Rather than all participants completing conditions in the same order (A then B), participants are divided: half complete A then B, half complete B then A. This distributes order effects evenly across conditions rather than consistently benefiting one. Counterbalancing does not eliminate order effects — it balances them. For more than two conditions, Latin square designs ensure each condition appears in each position equally often.
- Laboratory experiment
- Conducted in an artificial, controlled setting where the researcher manipulates the IV. Maximises control of extraneous variables and therefore internal validity. The trade-off is ecological validity: behaviour in an artificial setting may not reflect everyday behaviour. Associated with high internal validity but sometimes lower ecological validity.
- Field experiment
- Takes place in participants' natural environment with the IV still actively manipulated by the researcher. Preserves ecological validity but sacrifices some control over extraneous variables. Classic example: Bickman's (1974) uniform study, which manipulated authority by having a confederate in different uniforms give instructions to passers-by in a real street setting.
- Natural experiment
- The IV varies naturally or through circumstances beyond the researcher's control — policy changes, natural disasters, the introduction of technology to a community. Random allocation is impossible; participants are determined by circumstance. Has high ecological validity but cannot establish causation with the confidence of true experiments. Classic example: Charlton et al.'s study of children's behaviour before and after television was introduced to the island of St Helena.
- Demand characteristics
- Cues in the experimental situation that allow participants to guess the study's purpose, leading them to alter their behaviour in line with (or deliberately against) perceived expectations. Threaten internal validity because the DV may reflect participants' responses to their inferences about the study rather than to the IV alone. Minimised by single-blind procedures and ethical deception.
- Double-blind procedure
- Both participants and the researchers collecting or scoring data are kept unaware of condition allocation. Addresses demand characteristics (participants cannot adjust behaviour to perceived expectations) and experimenter bias (researchers cannot unconsciously treat participants differently or interpret ambiguous data in line with their expectations). Commonly used in drug trials where neither the patient nor the administering clinician knows who received the active drug.