Research Methods in Psychology

Alternative Hypothesis

• Leads to spuriousness (other causes for IV and DV)
• Qualitative research doesn't use hypothesis testing in the same way as quantitative research

Antecedent Variable

• Comes before the independent variable
• Can help explain the relationship between the independent and dependent variables
• Example: education level may explain the relationship between age and income

Arrow Diagram

• Pictorial representation of explanatory scheme.
• Shows how variables connect with arrows.
• Represents hypothesized causal relationship.

Bar Chart

• Graphical display of frequency or percentage distribution.
• Height represents proportion/percentage of observations in each category.
• Useful when showing the distribution across a few categories.

Case Study

• In-depth study of a single case or small number of cases.
• Non-experimental design.
• Researchers have limited control over events (often qualitative).
• Hypothesis generating or hypothesis testing.
• Idiographic (not generalizable, specific to understanding one event).

Hypothesis Generating Case Study

• Looking at cases to propose a hypothesis.

Hypothesis Testing Case Study

• Examining cases to prove a hypothesis.

Idiographic Case Study

• Focuses on one specific event rather than generalizability.

Categorical Measure

• Nominal or ordinal level.
• Ordinal - has a meaningful order
• Nominal- does not have an order (classifications)

Causes of Effects Approach

• Starts with outcome and works backward to identify causes.

Central Tendency

• Most frequent, middle or central value in a frequency distribution.
• Mean, median, mode.

Classical Randomized Experiment

• Random assignment to control and experimental groups.
• Pretest and posttest for both groups.
• Treatment and control group differ only by stimulus.

Closed-Ended Questions

• Questions with predetermined answer options.
• Limits respondent's answers to select from provided options.

Cluster Sample

• Probability sample used when a list of all elements doesn't exist.
• Sampling frame initially consists of clusters of elements.

Confidence Interval

• Range of likely values for a population parameter.
• Estimates a range with a high degree of confidence.
• Wider range for higher level confidence.
• Narrower range for lower confidence level.

Confidence Level

• Degree of belief or probability that the estimated range includes the true population parameters

Constant

• A concept or variable which values do not vary

Construct Validity

• Measures are valid when related to other measures of the same concept..
• Systematically analyzes/measures data by either reading or automated computer programs.

Content Analysis

• Systematically analyzes records (text, images, audio, video) to transform data into quantifiable data.

Content Validity

• Captures all components of the concept when evaluating a measure.

Control Group

• Group not exposed to the experimental treatment.

Convenience Sample

• Selection of elements that are easily accessible.
• Less reliable than other techniques.
• A form of non-probability sample.

Correlation

• Association between two variables.
• Values of one variable vary systematically with values in another.

Cross Sectional Design

• Measurements of all variables at one point in time/period.

Cumulative Knowledge

• Builds upon previous findings and research techniques

Data Matrix (Data Set)

• Stores numerical values for variables in rows and columns.
• Rows are specific cases (individuals).
• Columns are different variables.

Dependent Variable

• Affected by the independent variable.

Descriptive Statistics

• Numerical summaries of the properties/ characteristics of a group of numbers.

Deviant Case

• Does exhibit all the factors except the outcome

Dichotomous Variable

• Variable with only two categories.

Direct Observation

• Observing behaviors in person (either lab or field).

Dispersion

• Measures how spread out data are from the central tendency

Disproportionate Sample

• A stratified sample with over or under representation of a characteristic.

Document Analysis

• Using written (or other forms of) materials as data sources.

Double-Barreled Questions

• Asks two questions in one.

Ecological Fallacy

• Incorrect inference about individuals based on group-level observations.

Effects of Causes Approach

• Starts with cause and works towards impact.
• Measures the extent of the effect of a potential cause.

Electronic Databases

• Collection of data from various sources (internet, digital archives).

Elite Interviewing

• Interviewing individuals with special knowledge or experience on topic of study

Empirical Research

• Verifiable research based on observation and experimentation.
• Data is observable.
• Propositions are supported by collected verifiable data.

Episodic Records

• Informal record keeping, not part of a formal system

Ethnography

• In-depth observation of a culture or social group

Experiment

• Research design manipulating a variable to observe effect on another.
• Involves the control of experimental and control groups
• Observation and analysis of results of manipulation.

Experimental Effect

• Changes in dependent variable caused by the independent variable
• Effect is often measured numerically.

Explanatory Knowledge

• Understandings of how and why particular phenomena occur.

External Validity

• Ability to generalize research findings to other contexts or groups.

Face Validity

• Measure appears to capture the construct accurately based on appearance.

Falsifiability

• Statement capable of being disproved by evidence.
• Statements which cannot be tested are not falsifiable.

Field Experiment

• Conducted in a natural setting.
• Participants engaging in activities as part of their daily lives.

Frequency Distribution

• Lists count of observations (or percentage) in a set of categories.

General Knowledge

• Knowledge applicable to many cases, rather than one or a few

Histogram

• Bar graph showing the frequency distribution of data in intervals.
• Height and area proportional to frequency distributions

Hypothesis

• Testable statement about a relationship (often between independent/dependent variables).

Hypothesis Generating Case Study

• Case study to develop general propositions that can be tested later.

Idiographic Case Study

• Detailed description or explanation of a unique phenomenon.

Independent Variable

• The variable the researcher is manipulating or testing.

Indirect Observation

• Observing traces of behavior, rather than actual behavior.

Informed Consent

• Procedures ensuring voluntary participation in research by research subjects.

Institutional Review Board (IRB)

• Panel reviewing ethics of research involving humans.

Internal Validity

• Degree to which manipulation of the independent variable causes observed changes in the dependent variable.

Interval-Level Measure

• Numerical values with intervals of equal size.
• Example: temperature, test scores.

Intervening Variable

• A variable between independent and dependent variable's effects

Interview Data

• Collected through questions, interviews, or conversations.
• Can be structured or unstructured.

Large-N Studies

• Examine many cases.
• Collect a lot of data points.
• Often use quantitative measurements.

Leading Question

• Leads respondents toward certain answers.

Least Likely Case (Hard Case)

• Case where theory least likely to apply (most challenging to test).

Level of Measurement

• Category or type that describes a numerical variable.

Linear Regression

• Statistical approach to model linear relationship between variables.
• Used to analyze relationships between dependent/independent variables

Literature Review

• Critical examination of existing literature relating to a topic.
• Summarises previous work relevant to current research
• Contexts current research.

Mean

• Average of a set of numerical values

Measurement Bias

• Systematic error in measurement, either over or under estimating values

Median

• Middle value in a sorted set of numerical values.

Method of Agreement

• Researchers identify factors common to all cases that share an outcome.

Method of Difference

• Researchers compare cases with similar characteristics but different outcomes, looking for the distinguishing factor.

Mode

• Most frequent value in a set of data.

Most Likely Case (Easy Case)

• Theory confirms outcome using easy-to-study cases

Natural Experiment

• Observing naturally occurring events.
• Similar to a quasi-experimental design.

Negative Relationship

• Relationship in which high values of one variable are associated with low values of another.

Nominal-Level Measure

• Variables classified into categories with no inherent order..

Non-Participant Observation

• Researchers observe behaviors from a distance (not participating).

Non-Probability Sample

• Sample for which each unit (person, item) has varying or unknown probability of selection

Normative Knowledge/Questions

• Evaluative and concerned with ideals, rather than describing what is.

Normal Distribution

• Bell-shaped curve describing the frequency of data.
• Mean, median, and mode are all the same.

Null Hypothesis

• Statement that there is no relationship between variables for a target population

Observational Study

• Researcher observes and records data without manipulating any variables.
• No direct control over or manipulation of variables.
• Focuses on observing naturally occurring variables

Open-Ended Questions

• Questions allowing respondents to answer freely about a topic using their own words

Operational Definitions

• Precise, measurable statements that describe how variables are to be measured and recorded in a study..

Operationalization

• Deciding which methods to use to measure variables

Outlier

• Value significantly different from other data points in a dataset.

Participant Observation

• Researchers actively participate in the observation rather than just watching (participating in group activity).
• Often qualitative.

Parsimony

• Preference for simpler explanations of phenomena.

Peer Review

• Process whereby other experts evaluate research submissions in a field for quality analysis before publication.

Population

• Entire set of units or cases that researchers are interested in.

Population Parameter

• A measurable characteristic of a population.
• Mean, proportion are examples.

Positive Relationship

• Relationship where a value in one variable increases with a value in another

Precision

• How informative/detailed a measurement is
• Extent of data values in measurement, how granular or detailed data.

Pretest

• Measurement of variables before any manipulation in a study
• Measurement made before manipulation or intervention, prior to the main study

Primary Data

• Directly observed data collection

Probability Sample

• Each case in the target population has a known (typically equal) chance of selection.
• All cases are not excluded.

Process Tracking

• Researchers trace a causal path from initial cause(s) to final outcome.
• Often detailed and in depth

Proportionate Sample

• Cases within the sample reflect their proportions in the population.

Quota Sample

• Reflecting the population's characteristics but selected using convenience.
• Accounts for subgroups in the population.

Randomization

• Assigning subjects to conditions/groups by chance

Ratio-Level Measure

• Highest level of measurement, with a meaningful zero point and equal values.

Reactivity

• Change in behavior when participants know they're being observed.

Reliability

• Consistency of results when measurements are repeated.
• Results should not differ too much under any one condition or from study to study.

Research Hypothesis

• Statement of the expected relationship between variables in a study.

Research Design

• Plan for how a study will be performed.
• Details of sampling methodology, methods of measurement, analysis procedures

Response Rate

• Proportion of selected (all/potential) participants who actually participate in the study

Running Record

• Records maintained over a period of time, showing changes in variables over time.

Sample

• Subset of the population
• Selection of elements of interest.
• Represents overall population characteristics.
• Selection criteria are important

Sample Bias

• Elements of the population are systematically excluded or over-represented in the sample/results

Sampling Distribution

• Distribution of a particular sample statistic.
• Used for calculating confidence intervals.

Sampling Error

• Difference between a sample statistic and the population parameter.
• Random variation.

Sampling Frame

• List of all cases that comprise the population; used in sample selection; needed for certain calculations.

Skewed Distribution

• Data points skewed either to the right or left, with an upward sloping tail in one direction or a downward sloping tail in the other.

Small-N Studies

• Focus on a few cases (qualitative).
• Detailed description and detailed analysis

Spurious Relationship

• Correlation between two variables is not because of a causal relationship but due to a third variable.

Standard Deviation

• Measures data's spread around the mean.

Stratified Sample

• Divided into subgroups (strata) then random sample from subgroups.
• Proportionate/ disproportionate.

Statistical Inference

• Using sample data to make conclusions about a population.

Statistical Significance

• Probability that obtained data is representative of the population.

Trend Analysis

• Examining how a variable changes over time.
• Plots and graphs.

Two-Sided Question

• Question inviting two alternative responses.

Type 1 Error

• Rejecting a true null hypothesis.

Type 2 Error

• Failing to reject a false null hypothesis

Unit of Analysis

• What (or whom) data of a study is collected on (e.g., people, groups, institutions)

Validity

• Accuracy of a measurement; does it measure what it is intended to measure?
• Face, content, construct.

Verification

• Confirmation or support of a statement using evidence or data.

Z-Score

• Tells us how many standard deviations a value is from the mean of a distribution.

Explore key concepts in research methods for psychology through this quiz. Topics include alternative hypotheses, antecedent variables, visual representation techniques like arrow diagrams and bar charts, and case studies. Test your knowledge on qualitative and quantitative research methodologies.