Research Methods 3 Flashcards

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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)

Participants may try to act like good participants. (correct)

Impossible to use in experiments with multiple independent variables.

Behavior changes based on previous exposures. (correct)

Order effects that can threaten internal validity. (correct)

Is the pretest/posttest a true within-groups design?

False Signup and view all the answers

What does construct validity of experiments assess?

It assesses the validity of both the independent and dependent variables. Signup and view all the answers

What is the significance of statistical validity in experiments?

It determines how well the data supports causal conclusions and whether the results are statistically significant. Signup and view all the answers

What does an effect size of d = 0.5 indicate?

Medium or moderate effect. Signup and view all the answers

What internal validity threat arises from participants dropping out of a study?

Attrition. Signup and view all the answers

What are demand characteristics in an experiment?

Cues that lead participants to guess the hypotheses of the study, potentially changing their behavior. Signup and view all the answers

What is one solution to prevent observer bias?

Use double-blind studies. Signup and view all the answers

An effect size in which d = ______

2 Signup and view all the answers

What can noise in an experiment indicate?

Too much variability within groups. Signup and view all the answers

What are multivariate designs?

Involving more than two measured variables. Signup and view all the answers

Which of the following are the three causal criteria?

Temporal precedence Signup and view all the answers

What do bivariate correlations show?

They show covariance but do not show temporal precedence or internal validity. Signup and view all the answers

What is a longitudinal design?

Measures the same variables in the same people at different points in time. Signup and view all the answers

What was the purpose of the TV violence and aggression study?

To collect data on aggression and interest in violent TV over time among children. Signup and view all the answers

What are cross-sectional correlations?

Tests to see whether two variables at the same point in time are correlated. Signup and view all the answers

What do autocorrelations measure?

The association of each variable with itself over time. Signup and view all the answers

What are cross-lag correlations?

Show whether the earlier measure of one variable is associated with the later measure of another variable. Signup and view all the answers

What are the three rules for causation in longitudinal studies?

Covariance, temporal precedence, internal validity. Signup and view all the answers

Why can't experiments be conducted in longitudinal designs?

Due to ethical concerns and practicality in assigning preferences. Signup and view all the answers

What does multiple regression help with?

It helps rule out some third variables and addresses questions of internal validity. Signup and view all the answers

What does measuring third variables in multiple regression designs prevent?

It prevents the third variable problem. Signup and view all the answers

What are the steps in studying 3 or more variables with multiple regression designs?

Choose the criterion variable and identify the predictor variables. Signup and view all the answers

What is the significance of a p-value for beta?

When p is less than 0.05, the beta is considered statistically significant. Signup and view all the answers

What are mediators in research?

Variables that explain the relationship between two other variables. Signup and view all the answers

What is the purpose of conducting experiments?

To understand the nature of relationships between variables and potentially influence outcomes. Signup and view all the answers

What are design confounds?

An experimenter's mistake in designing the independent variable that introduces alternative explanations. Signup and view all the answers

What are order effects in within-group designs?

When exposure to one condition influences responses to subsequent conditions. Signup and view all the answers

What is counterbalancing?

A technique used to avoid order effects. Signup and view all the answers

What are matched groups?

Groups that prevent selection effects by equating participants based on certain characteristics. Signup and view all the answers

What is the difference between independent groups designs and within-group designs?

Independent groups have different participants for each level of the independent variable while within-group designs use the same participants for all levels. Signup and view all the answers

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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