Questions and Answers
What is full counterbalancing?
In a within groups experiment when it only has two or three levels of an independent variable, full counterbalancing is when all possible condition orders are represented.
What is partial counterbalancing?
Only some of the possible condition orders of a within groups experiment are represented.
Which of the following are disadvantages of within groups designs? (Select all that apply)
Is the pretest/posttest a true within-groups design?
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What does construct validity of experiments assess?
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What is the significance of statistical validity in experiments?
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What does an effect size of d = 0.5 indicate?
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What internal validity threat arises from participants dropping out of a study?
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What are demand characteristics in an experiment?
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What is one solution to prevent observer bias?
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An effect size in which d = ______
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What can noise in an experiment indicate?
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What are multivariate designs?
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Which of the following are the three causal criteria?
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What do bivariate correlations show?
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What is a longitudinal design?
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What was the purpose of the TV violence and aggression study?
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What are cross-sectional correlations?
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What do autocorrelations measure?
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What are cross-lag correlations?
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What are the three rules for causation in longitudinal studies?
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Why can't experiments be conducted in longitudinal designs?
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What does multiple regression help with?
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What does measuring third variables in multiple regression designs prevent?
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What are the steps in studying 3 or more variables with multiple regression designs?
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What is the significance of a p-value for beta?
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What are mediators in research?
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What is the purpose of conducting experiments?
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What are design confounds?
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What are order effects in within-group designs?
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What is counterbalancing?
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What are matched groups?
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What is the difference between independent groups designs and within-group designs?
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Study Notes
Multivariate Designs
- Involves more than two measured variables, offering a comprehensive analysis of relationships between variables.
Three Causal Criteria
- Covariance: Indicates a relationship where A is associated with B.
- Temporal Precedence: A must precede B in time.
- Internal Validity: A is the sole cause of B, ruling out third variables.
Bivariate Correlations
- Demonstrate covariance but lack temporal precedence and internal validity due to potential third variable influence.
Longitudinal Design
- Measures the same variables across time intervals, establishing temporal precedence; often utilized in developmental psychology.
TV Violence and Aggression Study
- A longitudinal and multivariate study tracking 875 children over 10 years, examining aggression and violent TV interest.
Cross-Sectional Correlations
- Evaluates correlations between variables at a single time point, lacking directionality in causal relationships.
Autocorrelations
- Assesses a variable's correlation with itself over time, clarifying temporal sequence.
Cross-Lag Correlations
- Show associations between earlier measures of one variable and later measures of another, aiding in identifying causal direction.
Longitudinal Study Rules for Causation
- Establish covariance through significant correlations, ensure temporal precedence via multiple time measurements, and assess internal validity by controlling for third variables.
Limitations of Longitudinal Experiments
- Ethical considerations and practicality restrict random assignments and manipulations, making them unsuitable for certain conditions.
Multiple Regression
- A multivariate approach addressing internal validity by controlling for third variables, helping to understand relationships between key variables.
Ruling Out Third Variables
- Additional measurements in a correlational study help mitigate third variable issues impacting relationships.
Steps in Multiple Regression Studies
- Identify the criterion (dependent) variable and multiple predictor (independent) variables potentially influencing it.
Beta in Multiple Regression
- Each predictor has a beta value indicating its relationship strength; analysis is dependent on other included variables.
Significance of P Value for Beta
- A p value less than .05 indicates statistical significance; otherwise, it is considered non-significant.
Benefits of Adding Predictors in Regression
- Controls for multiple third variables simultaneously and enhances understanding of which factors most influence the outcome.
Phrasing Regression Findings
- Common terminologies include “controlled for,” “taking into account,” and “correcting for,” indicating adjustments made in analyses.
Causation and Regression
- While regression provides insights into internal validity, it cannot establish causation due to its inability to clarify temporal precedence.
Parsimony in Scientific Theories
- Advocates for simple explanations of phenomena, minimizing exceptions for clearer understanding.
Causality and Parsimony
- A pattern backed by parsimonious explanations, such as the link between cigarette smoking and cancer, underscores efficient causal inference.
Media Representation of Parsimony
- Often misrepresents scientific studies, focusing on isolated findings instead of showcasing the broader research context.
Mediator Variables
- Explain the relationship between two variables, offering insight into the mechanisms of causation.
Distinction Between Mediators and Third Variables
- Third variables are often nuisances, while mediators are central to understanding causal pathways.
Mediators vs. Moderators
- Mediators address the 'why' behind a link, while moderators evaluate the consistency of that link across different contexts or individuals.
Multivariate Designs and Four Validities
- Considerations include construct validity, external validity, internal validity, and statistical validity to ensure rigorous analyses.
Purpose of Experiments
- Aim to deepen understanding of variable relationships and facilitate the design of effective interventions.
###Experiment Characteristics
- Involves manipulating at least one variable to measure its effect on another.
Control Variables
- Aim to keep constant during an experiment to maintain internal validity, isolating effects of the independent variable.
Causal Rules Addressed by Experiments
- Experiments fulfill the requirements of covariance, temporal precedence, and internal validity.
Understanding Covariance in Experiments
- Evaluates relationships through distinct levels of the independent variable, as highlighted by group outcome differences.
Comparison Groups
- Provide context for evaluating the independent variable's impact, essential for establishing meaningful covariance.
Designing Independent Variable Levels
- Incorporate control, treatment, and comparison groups to clarify effects of independent variables.
Temporal Precedence in Experiments
- Researchers control the order of variable presentation, ensuring that cause precedes effect more effectively than correlational methods.
Internal Validity Importance
- Critical for ruling out alternative explanations and confirming causal relationships in findings.
Confounds
- Threats to internal validity resulting from extraneous variables influencing results.
Design Confounds
- Errors in experimental design leading to systematic variation which can confuse the relationship being tested.
Variability Types
- Systematic Variability: Discrepancies that affect groups unevenly, damaging internal validity.
- Unsystematic Variability: Random variations across conditions that may introduce unrelated issues but do not harm internal validity.
Selection Effects
- Arise when participant characteristics systematically differ across independent variable levels; can occur through non-random assignment.
Matched Groups
- A method to reduce selection effects by pairing participants with similar characteristics across groups.
Independent Group Designs
- Participants are assigned to different independent variable levels, enhancing group comparison integrity.
Within-Group Designs
- Single participant group exposed to all independent variable levels, allowing for direct comparisons of responses.
Posttest Only Design
- Participants are only assessed once after being assigned to independent variable groups.
Pretest/Posttest Design
- Participants evaluated twice—before and after exposure to the independent variable, useful for assessing group equivalence.
Design Variability Considerations
- Advantages and disadvantages of posttest-only versus pretest/posttest designs affect the study's robustness and insights.
Concurrent Measures
- Involves exposing participants to all independent variable levels around the same time for a singular preferred measurement outcome.
Repeated Measures
- Participants are assessed multiple times against different independent variable levels, helping to gauge consistent changes.
Pros and Cons of Simple Experiments
- Independent Group Designs: Require larger samples but avoid contamination.
- Within-Group Designs: Require fewer participants but may introduce order effects and demand characteristics.
Covariance, Temporal Precedence, and Internal Validity in Within-Group Designs
- Strong for establishing covariance and temporal order but face risks from order effects impacting validity.
Order Effects
- Threaten internal validity as previous exposures to conditions alter responses to subsequent conditions.
Practice and Carryover Effects
- Practice Effects: Improvements in performance due to familiarity; fatigue may also occur.
- Carryover Effects: Previous condition experience may carry over and influence current performance.
Counterbalancing
- Strategies employed to mitigate order effects by varying condition presentation order across participants.### Counterbalancing in Experiments
- Full counterbalancing represents all possible condition orders in within-groups experiments, effective with two or three levels of an independent variable.
- Partial counterbalancing features a subset of condition orders, often randomized for each participant.
Disadvantages of Within-Groups Designs
- Subject to order effects that can compromise internal validity, addressed through counterbalancing.
- In some cases, within-groups designs are impractical; e.g., teaching biking cannot reverse learning.
- Behavior may change based on exposure to previous conditions, affected by demand characteristics, where participants guess hypotheses, potentially skewing results.
Design Types
- Pretest/Posttest designs do not qualify as true within-groups designs; they don’t expose participants to every level of the independent variable.
Construct Validity
- Construct validity evaluates the adequacy of both independent and dependent variables.
- Manipulation checks assess how effectively independent variables are manipulated, and pilot studies test manipulations before full-scale experiments.
Theory Testing and Validity
- The validity of study constructs is measured against the theory being tested, ensuring results support the proposed hypothesis.
External Validity
- External validity addresses generalizability of causal claims to other populations and situations, often requiring multiple studies for broader applicability.
Statistical Validity
- Evaluates how well data backs causal conclusions; statistical significance indicates meaningful relationships between variables.
- Effect size quantified using Cohen's d: small (0.2), medium (0.5), large (0.8), with larger values indicating stronger effects.
Internal Validity
- Central to experimental design, ensuring outcomes are not influenced by alternative explanations.
- Key questions include checking for design confounds, controlling selection effects, and managing order effects.
Internal Validity Threats in Pretest/Posttest Designs
- Maturation effect refers to natural changes occurring over time, preventable by including comparison groups.
- History effect happens when external events impact all subjects simultaneously, also mitigated through comparison.
- Regression to the mean describes the tendency for extreme scores to return to average levels, addressed by including comparison groups.
- Attrition represents participant dropout, especially problematic when specific groups leave; prevention involves eliminating affected participants.
- Testing may cause participants to become better or worse through repeated exposure, countered by comparison groups or post-test only designs.
- Instrumentation threats occur when measuring tools change, necessitating calibration or post-test only designs.
Bias and Demand Characteristics
- Observer bias arises when researchers’ expectations influence results, avoidable through blind or masked designs.
- Demand characteristics lead to behavioral changes as participants infer study purpose, countered by double-blind approaches.
- Placebo effects occur when participants improve due to belief in treatment efficacy; mitigated using double-blind placebo control studies.
Factors Contributing to Null Results
- Null results may stem from weak manipulations that fail to impact dependent variables or from insensitive measures that cannot detect differences.
- Ceiling and floor effects limit measurable outcomes, with manipulation checks helpful for highlighting weak manipulations.
- Noise entails unsystematic variability causing null results, while measurement error reflects inaccuracies in dependent measures.
- Situation noise introduces external distractions that distort results by introducing variability.
Interaction Effects
- An interaction effect occurs when the impact of one independent variable differs based on another, mathematically defined as a difference in differences.
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