Bivariate and Multivariate Correlational Research

Bivariate Correlations

Associations involving exactly two variables.

Construct Validity

Assessment of how well variables are measured.

Effect Size

Describes strength of an association.

Sample Size

Number of observations in a study.

95% Confidence Interval

Range where true correlation likely falls.

Statistical Significance

Finding unlikely from a population with zero association.

Moderating Variables

Variables altering the relationship between two others.

Covariance

Indicates whether variables are related.

Temporal Precedence

Establishes which variable occurs first.

Internal Validity

Assesses if a third variable explains the relationship.

Longitudinal Designs

Measure same variables over multiple time points.

Multiple Regression

Analyzes relationships among multiple variables simultaneously.

Mediating Variables

Variables explaining the relationship between two others.

External Validity

Generalizability of study findings to other contexts.

Face Validity

Extent measure appears effective for its purpose.

Concurrent Validity

Measure correlates with established measures of the same construct.

Discriminant Validity

Measure does not correlate with unrelated constructs.

Convergent Validity

Measure correlates with similar constructs.

Replication

Repetition of study to confirm findings.

Benchmarks

Standards for comparing effect sizes.

Third-Variable Problem

Unaccounted variable affecting relationship between two variables.

Causal Claims

Hypotheses about cause-and-effect relationships.

Cross-Sectional Correlations

Correlations between variables measured simultaneously.

Temporal Precedence

Establishing which variable precedes the other.

Covariance

Significant correlation indicates variable relationship.

Longitudinal Studies

Research designs assessing variables over time.

Internal Validity

Degree to which results are attributable to the study.

Multiple Regression

Statistical method analyzing relationships among multiple variables.

Criterion Variable

Outcome variable being predicted in regression analysis.

Predictor Variable

Variable used to predict the criterion variable.

Beta

Coefficient indicating strength and direction of relationships.

Statistical Significance

Probability that results are not due to chance.

Confidence Interval (CI)

Range estimating the true value of a parameter.

Design Confounds

Variables that unintentionally vary with independent variable.

Mediators

Variables explaining the relationship between two others.

Third Variables

External factors affecting the correlation between two variables.

Regression Analysis

Technique to determine relationships among variables.

Control Variables

Variables held constant to prevent confounding effects.

Experimental Variables

Includes manipulated and measured variables in experiments.

Independent Variable (IV)

Variable manipulated by the researcher.

Dependent Variable (DV)

Variable measured to assess effect of IV.

Significance Level

Threshold (e.g., p < .05) for determining statistical significance.

Longitudinal Design Criteria

Covariance, temporal precedence, and internal validity.

Regression Predictors

Variables added to improve prediction accuracy.

Systematic variability

Consistent differences affecting study outcomes.

Unsystematic variability

Random differences not affecting study outcomes.

Selection effect

Differences in participant characteristics across groups.

Random assignment

Participants randomly allocated to groups.

Matched groups

Participants paired based on similar characteristics.

Pretest/Posttest Design

Measures participants before and after treatment.

Order effects

Previous conditions influence responses to subsequent conditions.

Practice effects

Improvement due to repeated exposure to tasks.

Carryover effects

Impact of one condition persists into another.

Counterbalancing

Method to control order effects in experiments.

Full counterbalancing

All possible orders of conditions are used.

Partial counterbalancing

Only some orders of conditions are used.

Design confound

Unintentional variable affecting independent variable outcomes.

Observer bias

Researcher expectations influence participant behavior.

Demand characteristics

Participants alter behavior based on perceived study aims.

Placebo effects

Participant's belief affects their response to treatment.

History threat

External events influence study outcomes over time.

Regression threat

Extreme scores move closer to the mean over time.

Testing threat

Repeated testing affects participant performance.

Instrumentation threat

Changes in measurement tools affect study results.

Selection-history threat

External factors affect only one experimental group.

Selection-attrition threat

Loss of participants affects only one group.

Comparison group

Group with equal exposure, no treatment given.

Regression to the mean

Extreme scores move closer to average over time.

Attrition

Dropout of extreme cases affects group averages.

Testing

Repeated testing influences participants' performance.

Instrumentation

Changes in measurement tools affect results.

Design confound

Unintentional variable varies with independent variable.

Selection effect

Systematic differences in participant groups.

Observer bias

Researcher expectations influence ratings of behavior.

Order effect

Previous conditions affect responses in repeated measures.

Demand characteristic

Participants alter behavior based on study expectations.

Practice effects

Improvement due to familiarity with the test.

Fatigue effects

Decline in performance due to tiredness.

Control variables

Variables kept constant to isolate independent variable.

Random assignment

Participants randomly placed in different groups.

Posttest

Assessment after treatment to measure effects.

Pretest

Assessment before treatment to establish baseline.

Extreme scores

Scores significantly higher or lower than average.

Leniency in coding

Coders become less strict over time.

Independent variable

Variable manipulated to observe effects on dependent variable.

Dependent variable

Variable measured to assess impact of independent variable.

Systematic variation

Consistent differences that affect experimental outcomes.

Experimental group

Group receiving treatment in an experiment.

Maturation

Natural development affecting experimental group outcomes.

Purpose of the Study

Research aim influencing design and participant guessing.

Comparison Group

Group with equal maturation, no treatment received.

History

External factors altering experimental group over time.

Placebo Effect

Improvement due to belief in treatment efficacy.

Regression to the Mean

Extreme scores normalize over time due to chance.

Attrition

Changes due to extreme cases dropping out systematically.

Null Effects

Lack of difference from independent variable manipulation.

Between-Groups Difference

Insufficient difference between experimental groups.

Within-Groups Variability

Variability obscuring differences between groups.

Weak Manipulations

Ineffective treatment leading to null effects.

Insensitive Measures

Tools failing to detect group differences.