exam 202 #2

Variables

• Independent Variables (IV): The variable that is manipulated or categorized to observe its effect on the dependent variable.

  • Levels of IVs: Different variations or categories of the independent variable.

• Dependent Variables (DV): The outcome or response measured in the study; it depends on the manipulation of the independent variable.

• Subject Variables: Characteristics of participants (e.g., age, gender) that are not manipulated but may influence the results.

• Measured vs. Manipulated Variables:

  • Measured: Variables that are observed or recorded without intervention (e.g., height, mood).

  • Manipulated: Variables that are intentionally altered in an experiment (e.g., dosage of a drug).

• Confounded Variables: Variables that are unintentionally related to both the independent and dependent variables, making it difficult to draw clear conclusions.

• Quantitative Variables: Variables that represent a measurable quantity (e.g., height, weight, time).

• Categorical Variables: Variables that categorize or classify data into distinct groups (e.g., gender, type of therapy).

  • Continuous Variables: Variables that can take any value within a range (e.g., temperature, time).

  • Discrete Variables: Variables that have distinct, separate values (e.g., number of children, number of cars).

Examples:

Independent Variable (IV): The factor that is manipulated or categorized to see its effect.

Dependent Variable (DV): The outcome that is measured based on the IV .

• Aggression toward minorities as a function of intelligence

IV: Intelligence

DV: Aggression toward minorities

• Extraversion predicts number of breakups initiated

IV: Extraversion

DV: Number of breakups initiated

• School performance as a function of inhibition

IV: Inhibition

DV: School performance

• The effect of anxiety on female affiliation in the cafeteria

IV:Anxiety

DV: Female affiliation in the cafeteria

Types of Measurement Scales

• Nominal Scale: Categorical data with no order (e.g., gender, colors).

• Ordinal Scale: Categorical data with a meaningful order but no fixed intervals between categories (e.g., rankings).

• Interval Scale: Numeric data with equal intervals but no true zero (e.g., temperature in Celsius).

• Ratio Scale: Numeric data with equal intervals and a true zero (e.g., weight, height).

Reliability and Validity of Measurements

• Reliability: The consistency or stability of a measure.

  • Test-retest reliability: The consistency of a measure over time.

  • Internal consistency: The consistency of results across items within a test.

• Validity: The degree to which a measure assesses what it claims to measure.

  • Face Validity: Whether a measure appears to measure what it's supposed to measure.

  • Content Validity: Whether the measure covers all aspects of the concept being measured.

  • Criterion Validity: Whether a measure predicts an outcome it should theoretically predict (includes concurrent and predictive validity).

    • Concurrent validity: The measure's correlation with a relevant outcome at the same time.

    • Predictive validity: The ability of the measure to predict future outcomes.

Visualizing Data

• Frequency Bar Graphs: A graph showing the frequency of different categories.

• Frequency Distributions: A representation of how frequently different values appear in a dataset.

• Non-frequency Bar Graphs: Bar graphs used for comparing categories or groups (not based on frequency).

• Error Bars: Lines indicating the variability of data in a graph, usually representing the standard deviation or confidence interval.

• Histograms: A bar graph for continuous data, showing frequency distribution.

• Pie Chart: A circular chart divided into slices to represent proportions of a whole.

• Scattergrams (Scatter Plots): A graph that shows the relationship between two continuous variables.

• Line Graphs: A graph that shows data points connected by lines, often used for showing trends over time.

5 types of Validity

• Internal Validity: The extent to which the study accurately measures the relationship between the IV and DV, without influence from confounding variables.

• Construct Validity: The extent to which a measure truly measures the concept it intends to measure.

• External Validity: The extent to which the findings of a study can be generalized to other contexts, populations, or settings.

• Statistical Validity: The degree to which the statistical analysis used in the study is accurate and appropriate.

• Ecological Validity: The extent to which the findings of a study can be generalized to real-world settings.

Threats to Validity:

• Demand characteristics (History AKA role demand): External events that occur during the study and affect participants.

• Maturation: Changes that occur within participants over time.

• Regression to the mean (Testing Effects): Changes in participants' responses due to prior testing.

• Morality (AKA Attrition): Loss of participants over time.

• Diffusion (Instrumentation): Changes in the measurement tool or procedure over time AKA participant crosswalk/ leakages when treatment spreads from group A to group B

• Experimenter bias(Selection Bias): Non-random assignment to groups. SB:If bias in selecting groups leads to non equivalent groups. EB:When an experimenter unintentionally biases the result of the study 

Lab on Statistics

• Sample Size: The number of participants or observations in a study. Larger sample sizes typically yield more reliable results.

• Variance: A measure of how much scores in a dataset differ from the mean.

• Effect Size: The magnitude of the relationship or difference between variables, showing how meaningful a result is.

p-values: A statistical measure that helps determine the significance of the results. A p-value below 0.05 typically indicates a statistically significant result. (p- values no reliable unless you have a big amount of data)