PLSC 3603 Final Exam Study Guide: Research Designs & Stats

Posttest design

A research design in which the dependent variable

Cohort group

A group of people who all experience a significant event in roughly the same time frame.

Repeated measurement design

A research design in which measurements of independent and dependent variables are taken at the same time; naturally occurring differences in the independent variable are used to create quasi-experimental and quasi-control groups; and extraneous factors are controlled for by statistical means.

Multiple group design

Experimental design with more than one control and experimental group. Multiple-group designs may involve a posttest only or both a pretest and a posttest. They may also include repeated measurements.

Field experiment

Experimental designs applied in a natural setting.

Age effects

Effects associated with the process of becoming older.

Intervention analysis

A nonexperimental timeseries design in which measurements of a dependent variable are taken both before and after the 'introduction' of an independent variable.

Survey design unit of analysis

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Quantitative research designs

Use systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques. They emphasize identifying causal relationships.

Posttest-Only Control Group Design

Participants are randomly assigned to either a treatment or control group.

Mean

Average of the set.

Median

Order the set in numerical order and find the middle number.

Mode

Number that appears the most in the set.

Post-treatment data

Data collected after treatment to establish causality with high internal validity.

Pretest-Posttest Control Group Design

Both groups are measured before and after treatment, allowing comparison of change over time and across groups.

Repeated-Measurement (Time-Series) Design

Multiple pre- and post-treatment measurements are taken to enhance understanding of patterns and trends.

Multiple-Group Design

Includes more than one treatment group, enabling comparison across different interventions or dosage levels.

Field Experiments

Conducted in real-world environments, maintaining random assignment and providing high external validity.

Natural Experiments

Treatment assignment is beyond the researcher's control but resembles randomization, offering high external validity.

Quasi-Experimental Designs

No random assignment; groups are pre-existing, requiring careful statistical controls for confounders.

Observational Designs

Research designs that observe variables without manipulation.

Cross-Sectional Design

Observes variables at a single point in time.

Longitudinal (Time-Series) Design

Observes variables over time to detect trends or changes.

Intervention Analysis

Measures the impact of a specific event or policy.

Trend Analysis

Tracks long-term developments in variables like voter participation or public opinion.

Open-ended questions

Allow respondents to reply in their own words, useful for exploratory research.

Closed-ended questions

Provide fixed responses, making them easier to code and analyze.

Double-barreled questions

Ask two things at once, potentially confusing respondents.

Ambiguous questions

Vague or unclear questions that can lead to misinterpretation.

Leading questions

Suggest a correct answer, potentially biasing responses.

Wording Principles

Guidelines for crafting survey questions to ensure clarity and effectiveness.

Content Analysis

Systematically codes textual, visual, or audio material for research.

Units of Analysis

Categories used in content analysis, including recording units and context units.

Inter-coder reliability

Essential for validity in content analysis, ensuring consistent coding across different analysts.

Measures of Central Tendency

Statistical measures that summarize a set of data by identifying the central point.

Mean

Average value, sensitive to outliers.

Median

Middle value in a data set, robust to outliers.

Mode

Most common value in a data set, useful for categorical data.

Measures of Dispersion

Statistical measures that describe the spread of data points in a data set.

Range

Difference between the highest and lowest values in a data set.

Variance

Average squared deviation from the mean, indicating data spread.

Standard Deviation (SD)

Square root of variance; shows average distance from the mean.

Skewness

Describes asymmetry of distribution.

Positive skew

Tail on the right.

Negative skew

Tail on the left.

Normal Distribution

Bell-shaped, symmetrical curve.

Empirical Rule

68% of data within 1 SD, 95% within 2 SDs, 99.7% within 3 SDs.

Central Limit Theorem

Sampling distribution of the mean approaches normality as sample size increases.

Bar Graphs

Categorical comparisons.

Histograms

Distribution of interval/ratio data.

Pie Charts

Proportions of a whole.

Line Graphs

Trends over time.

Z-Score

Standardized score showing how many SDs a value is from the mean.

T-Test

Compares the means of two groups to see if they differ significantly.

Degrees of Freedom (df)

Number of values that are free to vary.

Confidence Intervals (CI)

Range where a parameter is likely to fall.

CI Formula

CI = estimate ± (critical value * standard error).

Null Hypothesis (H0)

No relationship or difference.

Alternative Hypothesis (H1)

There is a relationship/difference.

Type I Error (False Positive)

Rejecting a true H0.

Type II Error (False Negative)

Failing to reject a false H0.

Significance Level (α)

Common threshold: 0.05 (5% chance of Type I error).

P-value

Probability of observing the data if H0 is true.

Critical Values

Cutoffs that determine statistical significance.

Direction of Relationships

Positive (as X increases, Y increases), negative (as X increases, Y decreases).

Strength of Relationships

Determined by correlation coefficients.

Pearson's r

Measures strength and direction of linear relationships.

Chi-Square Test (χ2)

Tests relationship between two categorical variables.

Lambda

For nominal variables.

Gamma

For ordinal variables.

Cramér's V

Adjusted chi-square for strength of association.

Interaction Effects

Occur when the effect of one variable on an outcome depends on another variable.

Variance

Amount of spread in data.

Ordinary Least Squares (OLS) Regression

Predicts value of DV based on IV(s).

Regression Line (Line of Best Fit)

Minimizes squared differences between observed and predicted values.

Regression Coefficient (b)

Estimated change in DV for 1-unit change in IV.

R-Squared (R2)

Proportion of variance in DV explained by IV.

Residual

Difference between actual and predicted values.

Residual Sum of Squares (RSS)

Total of squared residuals.

Heteroscedasticity

Unequal residual variance, violating OLS assumption.