Research Methods in Psychology

Research Methods Summary

• Independent Variable: The variable manipulated in a study; the presumed cause.
• Dependent Variable: The variable measured in a study; the presumed effect.
• Case Report: Detailed description of one or more cases.
• Case-Control Study: Compares two groups: one with an outcome and a control group without the outcome.
• Cross-Sectional Study: A snapshot of a population at a specific point in time; often uses surveys.
• Descriptive Study: Describes characteristics of a population; commonly uses surveys and questionnaires to gather data.
• Correlational Study: Examines the relationship between variables, potentially for prediction. Positive and negative correlations exist
• Randomized Control Trial (RCT): Used to test interventions or treatments by randomly assigning participants to different groups.
• Meta-Analysis: Combines the results of multiple studies investigating similar variables to determine overall effect size.
• Internal Validity: The extent to which the study design ensures that only the independent variable affected the dependent variable.
• External Validity: The extent to which the study findings can be generalized to other populations or settings.
• Deductive Reasoning: Uses theories or general principles to understand specific cases; GENERAL → SPECIFIC
• Inductive Reasoning: Uses specific observations to develop general theories; SPECIFIC → GENERAL
• Primary Sources: Original reports or documents written by the individuals who conducted the research.
• Secondary Sources: Summaries or reviews of research studies performed by individuals other than the original researchers.
• Peer Review Process: A process to evaluate the quality of research findings in a research article before publication. Journals may have acceptance rates of 15-20%.
• MAXICON Principle: Maximize true variance and minimize error variance in study findings.
• Research Population: The entire group of individuals or elements the researcher is interested in.
• Inclusion Criteria: Specific characteristics that study participants must possess to be included.
• Exclusion Criteria: Specific characteristics that prevent study participants from being included.
• Power Analysis: Calculates the required sample size to detect a significant effect of the independent variable on the dependent variable.
• Random Sample: Ensures that every member of the population has an equal chance of being selected.
• Convenience Sample: Participants are selected based on convenience for the researcher.
• Stratified Random Sample: Ensures that subgroups in the population are proportionally represented.
• Systematic Sample: Every nth individual from a list of the population is selected.
• Assumptions: Essential beliefs or ideas that must be accepted as true during a study.
• Delimitations: Choices made by the researcher that narrow the scope of the study.

Data Collection Approaches

• Surveys vs. Direct Measures
• Open-Ended Questions: Allow for respondent-created answers; less structured.
• Closed-Ended Questions: Provide specific response options; more structured.
• Likert Scales: Provide a continuum of responses for measuring attitudes or opinions.
• Questionnaires: Provide efficient methods for gathering data from multiple individuals.
• Interviews: Gather data through direct interaction with participants (structured, semi-structured, or focused groups)

Measurement Validity

• Face Validity: Does the test appear to measure what it claims?
• Content Validity: Does the test cover all important aspects of the concept being measured?
• Criterion Validity: Does the test correlate with an external standard?
• Construct Validity: Does the test accurately measure the underlying concept?

Measurement Reliability

• Reliability: Refers to the consistency of a measure; the degree to which the study produces similar results under the same conditions.
• Poor Reliability: Participant variation, variation in the measurement instrument, variation in the person administering the test.
• High Reliability: Memory effect could be a reason for artificially high results

Data Types

• Nominal Data: Categorical data without inherent order (e.g., gender, political affiliation).
• Ordinal Data: Categorical data with inherent order (e.g., ranking).
• Interval Data: Numerical data with equal intervals between values; zero does not represent absence.
• Ratio Data: Numerical data with a true zero representing absence.
• Parametric Data: Statistical tests used with interval and ratio data.
• Nonparametric Data: Statistical tests used with nominal and ordinal data.

Descriptive Statistics

• Frequency Distributions: Summarizes how often different values occur.
• Mean: The average of a set of numbers.
• Median: The middle value in a sorted set of numbers.
• Mode: The most frequent value in a set of numbers.
• Range: The difference between the highest and lowest values.
• Standard Deviation: Measures the spread of scores around the mean.
• Percentile Rank: Indicates a person's position within a group based on their score.

Inferential Statistics

• Pearson's r (Correlation Coefficient): Measures the strength and direction of the relationship between two variables. A correlation coefficient can be interpreted as a strong, moderate or weak positive relationship.
• Positive Correlation: Both variables increase or decrease together.
• Negative Correlation: One variable increases while the other decreases.
• Null Hypothesis: States that there is no difference between groups or no relationship between variables.
• Alpha Level: Predetermined probability of making a Type I error (rejecting a true null hypothesis).