AP Psychology: Research Methods and Data Interpretation

Vocabulary flashcards covering key terms and definitions from the AP Psychology lecture notes on Research Methods and Data Interpretation.

Quantitative research

Research that uses numbers and statistics to draw conclusions. Think experiments and surveys with quantifiable data. Tip: Quantitative = Quantity (numbers).

Qualitative research

Research that focuses on descriptions and insights, not numbers. Examples include observations, interviews, and case studies. Tip: Qualitative = Quality (descriptions, not numbers).

Correlation coefficient

A number (between -1 and +1, symbolized by r) that shows how strong and in what direction two variables are related. Closer to -1 or +1 means a stronger link. Tip: Coefficient 'co-relates' strength and direction. r sounds like 'relationship rating'.

Positive correlation

When r is above 0, meaning as one variable goes up, the other variable also goes up (or both go down together). Tip: Positive = Proportional (move in the same direction).

Negative correlation

When r is below 0, meaning as one variable goes up, the other variable goes down (opposite directions). Tip: Negative = Opposite (move in inverse directions).

Naturalistic observation

Watching behavior as it naturally happens in its real-world setting, often without people knowing they are being watched. Tip: Naturalistic = Nature (real world, spontaneous).

Structured observation

Observing behavior in a controlled lab setting where the researcher can manage some environmental factors. Tip: Structured = Studio (controlled lab environment).

Coding

Organizing observed behaviors into clear, separate categories for consistent recording. Tip: Coding = Categorizing observations.

Inter-rater reliability

How much different observers agree when they categorize the same behaviors. Tip: Inter-rater = 'Among raters' agreement.

Participant observation

The researcher joins the group they are studying, either openly or secretly, to better understand their behavior. Tip: Participant = Researcher 'participates' in the group.

Hawthorne effect

People change their behavior because they know they are being watched in a study. Tip: Hawthorne = 'How they behave' when watched.

Longitudinal study

Studying the same group of people over a long time to see how they change or develop. Tip: Longitudinal = Long duration, same group.

Cross-sectional study

Comparing different age groups at one specific point in time to understand developmental differences. Tip: Cross-sectional = 'Cross-section' of ages, one time.

Case study

An extremely detailed and deep look at one specific individual or a very small group. Tip: Case = Focus on 'one case' in depth.

Survey

Asking a group of people a set of questions to gather their attitudes or opinions, often using numerical scales. Tip: Survey = 'Serve' up questions to many.

Nonresponse bias

When many people don't answer a survey, and those who do respond might have different opinions, skewing the results. Tip: Nonresponse = No answers, leads to a biased view.

Surveyor bias

When survey questions are phrased to nudge people towards a particular answer, influencing the results. Tip: Surveyor bias = The 'surveyor's' wording creates bias.

Experiments

Carefully planned tests designed to check a research idea or prediction. Tip: Experiments = 'Examine' a hypothesis.

Hypothesis

A testable prediction or educated guess about the relationship between two or more things. Tip: Hypothesis = 'High-level guess' that can be tested.

Controlled experiment

An experiment where everything is carefully controlled, and only the independent variable is changed to see its exact effect on the dependent variable. Tip: Controlled = 'Control all' other factors.

Field experiment

Experiments done in real-world settings, which means less control over variables compared to a lab. Tip: Field = 'In the field,' natural environment.

Natural experiment/quasi-experiment

An experiment where the researcher doesn't directly change variables but studies naturally occurring events and uses statistics to find relationships. Tip: Natural = Not manipulated by researcher, 'nature' does the changing.

Independent variables

The factor that the researcher changes or manipulates to see if it causes an effect. Tip: Independent = 'I' change it (the researcher).

Dependent variables

The factor that is measured to see if it was affected by the independent variable. Tip: Dependent = 'D'ata from the outcome 'depends' on the independent variable.

Control variables

Factors that are kept the same for everyone in the experiment to ensure fair comparison. Tip: Control = Kept 'constant' and 'controlled'.

Confounding variables

Hidden factors that can mess up the results by affecting the dependent variable, making it hard to tell if the independent variable truly caused the change. Tip: Confounding = 'Confusion' due to hidden factors.

Control group

The group in an experiment that doesn't get the special treatment; they serve as a baseline for comparison. Tip: Control = 'No treatment' baseline.

Experimental group

The group in an experiment that receives the special treatment or manipulation of the independent variable. Tip: Experimental = 'Experiences' the experiment's treatment.

Placebo effect

When a person feels better or experiences a change just because they believe they are receiving a real treatment, even if it's fake. Tip: Placebo effect = 'Power of belief' makes it work.

Placebo

A fake treatment (like a sugar pill), given to make people think they are receiving a real treatment. Tip: Placebo = 'Fake' treatment.

Single-blind experiment

An experiment where the participants don't know if they're in the control or experimental group, but the researchers do know. Tip: Single-blind = Only one party (participants) is blind.

Double-blind experiment

An experiment where neither the participants nor the researchers know who is in which group (control or experimental). Tip: Double-blind = Both parties are blind.

Sample

The smaller group of individuals who actually participate in the study. Tip: Sample = A 'small piece' of the population.

Population

The entire larger group that the study is interested in, from which the sample is taken. Tip: Population = The 'pool' of everyone.

Random sampling

Choosing participants completely by chance from the population to ensure the sample accurately reflects the larger group. Tip: Random sampling = 'Randomly choose' for a representative sample.

Representative sample

A sample that accurately mirrors the characteristics (like age, gender, background) of the larger population it came from. Tip: Representative = 'Represents' the whole population.

Random assignment

Putting participants into either the control or experimental group purely by chance, giving everyone an equal opportunity. Tip: Random assignment = 'Randomly assigned' to a group within the study.

Internal validity

How well an experiment shows that the independent variable truly caused the changes in the dependent variable, without other factors interfering. Tip: Internal = 'Inside' the experiment, showing cause and effect is real.

External validity

How much the results of a study can be applied to the real world and to other people and situations outside the experiment. Tip: External = 'Exit' the lab, apply to the real world.

Demand characteristics

Clues in a study that unintentionally tell participants how they 'should' behave, influencing their actions. Tip: Demand = Participants 'demand' to know the purpose, then act accordingly.

Observer-expectancy effect

When a researcher's expectations unintentionally influence how participants behave, even subtly. Tip: Observer-expectancy = Observer's 'expectations' bias results.

Operational definition

A clear, measurable description of how you will define and measure a specific variable in your study. Tip: Operational = How to 'operate' and measure it.

Descriptive statistics

Numbers that summarize and describe the main features of a data set, like averages and how spread out the data is. Tip: Descriptive = 'Describe' the data.

Inferential statistics

Statistics used to make predictions or draw conclusions about a larger population based on data from a sample. Tip: Inferential = 'Infer' (make conclusions) about the population.

Parameter

A measurable feature of an entire population (e.g., the average height of all adults). This is different from a 'statistic,' which describes a sample. Tip: Parameter = 'P'opulation characteristic.

Confidence interval

A range of values within which the true population parameter is likely to fall, based on sample data. Tip: Confidence = 'Confident' the true value is in this interval.

Null hypothesis

The assumption that there is no significant relationship or difference between the variables being studied (e.g., treatment has no effect). Tip: Null = 'No effect' or 'no relationship'.

Alternative hypothesis

The prediction that there is a significant relationship or difference between the variables; the opposite of the null hypothesis. Tip: Alternative = What you 'alternatively' hope to find (a relationship).

p-value

The probability of getting your results if the null hypothesis (no effect) were true. A low p-value means your results are unlikely due to chance, supporting the alternative hypothesis. Tip: p-value = 'Probability' of randomness. Low p = interesting result.

Significance level

A pre-set threshold (like 0.05 or 0.01) that the p-value must be lower than to consider a result 'statistically significant' and support the alternative hypothesis. Tip: Significance = Set 'limit' for deeming results important.

Statistically significant

When the p-value is lower than the significance level, meaning the results are likely not due to chance and support the alternative hypothesis. Tip: Significant = 'Small p' enough to matter.

Type I error

Incorrectly concluding there is an effect or relationship when there actually isn't one (a 'false positive'). Tip: Type I = 'I' see something that isn't there (false positive).

Type II error

Incorrectly concluding there is no effect or relationship when there actually is one (a 'false negative'). Tip: Type II = 'II' miss something that is there (false negative).

Line graph

A graph using lines to show how one variable changes in relation to another, often over time. Tip: Line graph = 'Lines' connect points to show trends.

Bar graph

A graph using rectangular bars of different heights or lengths to compare categories of numerical data. Tip: Bar graph = 'Bars' for comparisons.

Histogram

A type of bar graph that shows how often different data values or ranges occur (their frequency). Tip: Histogram = 'History' of frequency, bars touch.

Scatterplot

A graph with individual dots representing pairs of data points, used to visualize the relationship (or correlation) between two variables. Tip: Scatterplot = 'Scattered dots' show patterns.

Line of best fit

A straight line drawn through a scatterplot that best shows the overall trend or relationship between the two variables. Tip: Best fit = 'Fits best' through the scattered dots.

Measures of central tendency

Statistics (like mean, median, mode) that tell you the center or average value of a data set. Tip: Central tendency = What's 'typically central'.

Mean

The arithmetic average; you sum all values and divide by the count of values. Tip: Mean = 'Average' (mean, like being average).

Median

The middle value in a data set when all numbers are arranged in order from smallest to largest. Tip: Median = 'Middle' value (median of a road).

Mode

The value that appears most often in a data set. Tip: Mode = 'Most often'.

Outliers

Data points that are unusually far from the other data points; they are extreme values. Tip: Outliers = 'Out-of-line' points.

Measures of dispersion

Statistics (like range, variance, standard deviation) that tell you how spread out or varied the data in a set is. Tip: Dispersion = How 'dispersed' or spread out the data is.

Range

The difference between the highest and lowest values in a data set. Tip: Range = The 'range' from high to low.

Variance

A number showing how much individual data points typically vary from the average (mean). Tip: Variance = How much data 'varies' from the mean (squared).

Standard deviation

The average distance each data point is from the mean; it tells you about the typical spread of data. Tip: Standard deviation = 'Standard' amount of deviation from the mean (square root of variance).

Informed consent

Getting permission from participants after clearly explaining the study's purpose, procedures, potential risks, and their rights before they agree to take part. Tip: Informed consent = 'Consent' when 'informed'.

Debriefing

Explaining the full truth of a study to participants after it's over, especially if there was deception, and clarifying its real purpose and results. Tip: Debriefing = 'Briefing' after the study.

Confidentiality

Keeping participants' personal information private and protected, only accessible to authorized researchers. Tip: Confidentiality = Keep information 'confidential'.

Institutional review boards (IRBs)

Committees that review and approve research involving human participants to ensure it follows ethical guidelines