Chapter 10 - Introduction to Simple Experiments

Independent Variable

A variable that is manipulated or changed in an experiment to observe its effects on the dependent variable.

Dependent Variable

A variable that is measured or observed in an experiment, affected by changes in the independent variable.

Control Variable

A variable that is kept constant throughout an experiment to ensure that any observed effect is due to the manipulation of the independent variable. Varys systematically with the dependent variable.

Simple Experiement

• 1 independent variable, 2 levels

• 1 dependent variable

Criterion for a Causal Claim

• Covariance (Association)

• Temporal Precedence

• Internal Validity (Avoid confounds)

Confounds

A confound is a variable that varies systematically with the Independent variable and can affect the Dependent variable. Two types of confounds include design confounds and selection effects

Design Confounds

A design confound is a variable that unintentionally varies along with the independent variable, potentially leading to incorrect conclusions about causal relationships in an experiment. Keep everything constant but the IV.

Selection Effects

• Selection effects occur when participants in different experimental conditions differ in ways that could affect the outcome (ex night owls vs early birds), often due to the method of selecting or assigning participants to conditions. Something that is systematically different between participants and impacts results.

• To avoid, either employ random assignment or matched groups.

Matched Groups Design

A matched groups design is an experimental approach where participants are paired or grouped based on shared characteristics to ensure that these characteristics do not skew the results, thereby controlling for potential confounding variables.

Variability

Variability refers to how much the scores in a dataset differ from one another or from the mean, indicating the degree of spread or dispersion in the data. Two types of variability include Systematic Variability and Unsystematic Variability.

Systematic Variability

Systematic variability refers to differences in data that are consistent and can be attributed to the independent variable, affecting the outcome of an experiment in a predictable manner. It contrasts with unsystematic variability, which is random and does not correlate with any specific conditions.

Unsystematic Variability

Refers to random fluctuations in data that do not have a consistent pattern, often unrelated to the experimental manipulation, leading to noise in the results.

Extraneous Variable

An extraneous variable is any variable other than the independent variable that could influence the dependent variable, potentially confounding the results of an experiment if not controlled.

Extraneous Variables vs Confounds

Extraneous variables refer to any outside factors that can affect the dependent variable, whereas confounds are extraneous variables that are not controlled for and thus influence the outcome, making it difficult to determine the effect of the independent variable.

Independent Group Designs

Everyone is in one condition and there is random assignment.

• Posttest only

• Pretest-Posttest design

Posttest Design

A posttest design is a type of experimental design where the dependent variable is measured after the independent variable has been manipulated, allowing researchers to assess the effects of the treatment or intervention.

Pretest - Posttest Design

A pretest-posttest design is an experimental design that involves measuring the dependent variable both before and after the manipulation of the independent variable, allowing researchers to assess changes and effects due to the intervention.

Within-Groups Design

A within-groups design is an experimental design where all participants are exposed to every level of the independent variable, allowing for direct comparisons of effects within the same group.

• Repeated measures design

• Concurrent measures design

Cons:

• Doesn’t work for even study

• Demands characteristics

• Order Effects (Practice Effect, Fatigue Effect, Testing/contrast effect)

• Carryover effect

Repeated Measures Design

A repeated measures design is an experimental design in which the same participants are measured multiple times under different conditions, enabling researchers to analyze changes and effects within individuals across various levels of the independent variable (Condition 1 → DV → Condition 2 → DV).

Concurrent Measures Design

A concurrent measures design is an experimental design where participants are exposed to all levels of the independent variable simultaneously, allowing researchers to evaluate preferences or effects within a single session.

Practice Effect

The practice effect refers to the improvement in performance on a task that occurs due to repeated exposure or practice, potentially leading to biased results in within-groups research designs.

Fatigue Effect

The fatigue effect describes a decline in participant performance or responses due to the prolonged demands of a task or experiment, often resulting from repetition or time spent in the study.

Testing/Contrast Effect

The testing/contract effect occurs when participants' performance is influenced by prior testing or their expectations of the experimental procedure, potentially skewing results in repeated measures designs.

Carryover Effect

The carryover effect refers to the impact that a previous treatment or condition has on a participant's response in subsequent conditions, which can lead to confounding results in repeated measures experiments.

Counterbalancing

Counterbalancing is a technique used in experimental design to control for order effects by varying the order of treatment presentation across participants, thereby minimizing biases in results.

Latin Square

A Latin Square is a systematic method of arranging treatments in an experiment where each treatment appears exactly once in each row and column, effectively controlling for order and carryover effects.

Pilot Study

A pilot study is a small-scale preliminary investigation conducted to test the feasibility, time, cost, and adverse events involved in a research project before the main study is executed.

Manipulation Test

A manipulation test is an assessment used to determine whether an experimental manipulation has the intended effect on the participants or the outcome variable, verifying the effectiveness of the treatment.

Demand Characteristics

Demand characteristics refer to cues or signals in an experiment that inform participants about the purpose of the study, potentially influencing their behavior and responses.