Psych209 exam3 flashcards

Factorial Design

A research design that includes two or more independent variables (factors), allowing researchers to examine the effects of each variable independently and their combined effect on a dependent variable

Independent Variable (Factor)

A variable that is manipulated by the researcher in an experiment to observe its effect on the dependent variable

2x2 Design

A factorial design with two independent variables, each with two levels, producing four total conditions

Main Effect

The overall effect of one independent variable on the dependent variable, averaging across all levels of the other independent variable(s)

Interaction Effect

When the effect of one independent variable on the dependent variable differs depending on the level of another independent variable

Simple Main Effect

The effect of one independent variable at a specific level of another independent variable. Main Effect: "Overall, does Exercise Type affect weight loss?" (Averages everyone together).Definition: "Does Exercise Type affect weight loss specifically for people on the Keto diet?" (Looks only at the Keto group).

Independent Groups Design

A design where different participants are assigned to each condition of the experiment

Repeated Measures Design

A design where the same participants are measured across all conditions of the experiment

Mixed Factorial Design

A design that combines independent groups for one factor and repeated measures for another factor

Between-Subjects Factor: Participants are split into independent, mutually exclusive groups (e.g., receiving either Drug A or Placebo).
Within-Subjects Factor: Every participant experiences all levels of this independent variable (e.g., being tested at baseline, 1 month, and 6 months)

Linear Relationship

A relationship between two variables where the change in one variable produces a constant, proportional change in the other, appearing as a straight line on a graph

Monotonic Relationship

A relationship where as one variable increases, the other consistently either increases or decreases, but not necessarily at a constant rate

Curvilinear Relationship

A relationship where the effect of an independent variable on the dependent variable changes direction at some point, forming a curve rather than a straight line

Single-Case Experimental Design

A research design (also called single-subject or small-N design) that studies the behavior of one or a few individuals, using each subject as their own control

Baseline Period

The initial phase of a single-case design where the participant's behavior is observed and recorded before any treatment or intervention is introduced

Reversal Design (ABA)

A single-case design where baseline (A) is followed by a treatment phase (B), then a return to baseline (A) to confirm that the treatment caused the behavioral change

ABAB Design

An extension of the reversal design that adds a second treatment phase after the return to baseline, demonstrating the effect of the intervention more convincingly

Multiple Baseline Design

A single-case design that applies a treatment to different behaviors, subjects, or settings at different times to demonstrate the effect of the intervention without reversing treatment

Quasi-Experimental Design

A research design that lacks random assignment to conditions but still attempts to study the effect of an independent variable. use pre-existing, naturally formed groups.

Researchers use this approach when random assignment is unethical

One-Group Posttest-Only Design

A quasi-experimental design where a single group is measured only after a treatment, with no control group or pretest

One-Group Pretest-Posttest Design

A quasi-experimental design where one group is measured before and after a treatment, with no control group

History Effect

A threat to internal validity where an external event occurring between the pretest and posttest (not the treatment) causes the observed change

Maturation Effect

A threat to internal validity where natural changes within participants over time (e.g., growing older, fatigue) account for the observed change rather than the treatment

Testing Effect

A threat to internal validity where taking a pretest influences participants' performance on the posttest

Instrument Decay

A threat to internal validity where changes in the measurement instrument or observers over time produce inconsistent results

Regression Toward the Mean

A threat to internal validity where participants selected because of extreme scores tend to score closer to the average on subsequent measurements, regardless of treatment

Nonequivalent Control Group Design

A quasi-experimental design that includes a treatment group and a comparison group, but participants are not randomly assigned to groups

Nonequivalent Control Group Pretest-Posttest Design

A quasi-experimental design that measures both a treatment group and a nonequivalent control group before and after treatment, improving on having no control group

Nominal Scale

A scale of measurement that categorizes variables with no numerical value or rank order (e.g., gender, political party)

Ordinal Scale

A scale of measurement that ranks variables in order but does not have equal intervals between values (e.g., race placement, class rank)

Interval Scale

A scale of measurement with equal intervals between values but no true zero point (e.g., temperature in Celsius)

Ratio Scale

A scale of measurement with equal intervals and a true zero point, allowing meaningful statements about ratios (e.g., weight, height, reaction time)

Frequency Distribution

A summary of how often each score or category occurs in a dataset

Pie Chart

A circular graph divided into slices representing the proportion of each category in a dataset

Bar Graph

A graph using rectangular bars to compare frequencies or values across categories

Frequency Polygon

A line graph that displays the frequency of scores at each value across a distribution

Histogram

A bar graph where bars touch each other, representing the frequency of scores within intervals of a continuous variable

Mean

The arithmetic average of a set of scores, calculated by summing all scores and dividing by the number of scores

Median

The middle value in a ranked distribution of scores, used as a measure of central tendency when distributions are skewed

Mode

The most frequently occurring score in a distribution

Variability

The degree to which scores in a distribution differ from each other and from the mean

Standard Deviation

A measure of variability that indicates the average distance of scores from the mean

Variance

The average of the squared deviations from the mean

the square of the standard deviation

Range

A simple measure of variability calculated by subtracting the lowest score from the highest score in a distribution

Correlation Coefficient

A numerical index that describes the direction and strength of the relationship between two variables, ranging from -1.00 to +1.00

Pearson Product-Moment Correlation Coefficient (r)

The most common correlation coefficient, used when both variables are measured on interval or ratio scales

Restriction of Range

A situation where the range of scores on one or both variables is limited, which can artificially weaken or distort the observed correlation

Effect Size

A measure of the practical or theoretical significance of a relationship or difference, indicating the magnitude of an effect independent of sample size

Regression Equation

A mathematical equation that uses scores on a predictor variable to estimate scores on a criterion variable

Criterion Variable

The outcome variable being predicted in a regression equation

Predictor Variable

The variable used to predict the criterion variable in a regression equation

Multiple Correlation

A measure of the strength of the relationship between a criterion variable and a combination of two or more predictor variables

Multiple Regression

A statistical technique that uses two or more predictor variables to predict scores on a criterion variable

Mediation Model

A model in which the effect of an independent variable on a dependent variable is explained through an intervening (mediating) variable

Mediating Variable

A variable that explains the mechanism or process through which an independent variable influences a dependent variable

Moderating Variable

A variable that changes the direction or strength of the relationship between an independent variable and a dependent variable

Third Variable Problem

The possibility that a correlation between two variables is actually caused by a third, unmeasured variable affecting both

Structural Equation Modeling (SEM)

An advanced statistical technique used to test complex theoretical models that include multiple variables and pathways simultaneously

Inferential Statistics

Statistical procedures used to draw conclusions about a population based on data from a sample

Null Hypothesis

The hypothesis stating there is no effect or no difference between groups

the hypothesis that the researcher tries to reject

Research Hypothesis

The hypothesis that predicts a specific relationship or difference between variables that the researcher expects to find

Statistical Significance

The conclusion that an observed result is unlikely to have occurred by chance, based on a predetermined probability threshold (alpha level)

Alpha Level

The probability threshold (commonly .05) set by the researcher below which the null hypothesis will be rejected

Sampling Distribution

A theoretical distribution of a statistic (e.g., the mean) calculated from all possible samples of a given size from a population. acts as the mathematical bridge between a single sample you collect and the actual population you want to understand

t Test

An inferential statistical test used to determine whether the difference between two group means is statistically significant

Degrees of Freedom

number of independent variables or values that have the freedom to vary or change in a specific calculation or system

One-Tailed Test

A statistical test that places the entire probability of rejecting the null hypothesis in one tail of the distribution

used when predicting the direction of an effect

Two-Tailed Test

A statistical test that splits the probability of rejecting the null hypothesis between both tails of the distribution

used when not predicting the direction of an effect

Analysis of Variance (F Test)

An inferential statistical test used to compare means across two or more groups by analyzing the ratio of systematic variance to error variance

works by comparing the amount of variability between the different groups to the amount of variability within those groups.

Systematic Variance

the portion of total data variation caused by known, specific factors or identifiable relationships

Error Variance

Variability in scores that is due to random factors and individual differences, not the independent variable

Confidence Interval

A range of values around a sample statistic within which the true population value is likely to fall, with a specified level of confidence (e.g., 95%)

Type I Error

Rejecting the null hypothesis when it is actually true (a false positive)

its probability equals the alpha level

Type II Error

Failing to reject the null hypothesis when it is actually false (a false negative)

Power

The probability of correctly rejecting a false null hypothesis

the ability of a study to detect an effect when one truly exists

External Validity

The extent to which research findings can be generalized beyond the specific conditions of a study to other people, settings, and times

Generalization

The ability to apply research findings from a sample or setting to broader populations and contexts

Replicability

The ability of other researchers to repeat a study and obtain the same or similar results

Exact Replication

Repeating a study using the same procedures, materials, and participant characteristics as the original study to verify its findings

Conceptual Replication

Repeating a study using different methods or operationalizations to test the same underlying hypothesis and extend generalizability

Meta-Analysis

A quantitative technique that statistically combines results from many studies on the same topic to estimate the overall effect size

Biased Sampling

A sampling error that occurs when the sample is not representative of the population, limiting the generalizability of findings

Representative Sample

A sample that accurately reflects the characteristics of the population from which it was drawn

Nonrepresentative Sample

A sample that does not accurately reflect the broader population, limiting external validity

Pretesting Threat

A threat to external validity where exposure to a pretest affects participants' responses to the treatment, making results difficult to generalize to unpretested populations

Researcher Characteristics

Attributes of the researcher (e.g., age, sex, race) that may influence participant behavior and limit the generalizability of findings