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Quizzes > Science

Reliability in Psychology Quiz: Research Methods Essentials

Quick quiz to check your understanding for Research Methods Exam 1. Instant results.

Editorial: Review CompletedCreated By: Carlota LeonorUpdated Aug 26, 2025
Difficulty: Moderate
2-5mins
Learning OutcomesCheat Sheet
Paper art quiz on psychology research methods variables measurement levels error types validity definitions on sky blue

This reliability in psychology quiz helps you check how well you can identify variables, choose the right measurement levels, spot error types, judge validity, and write clear operational definitions. For wider practice, try our research methodology quiz or build foundation skills with the intro to psychology quiz.

What is the independent variable in a psychological experiment?
The variable manipulated by the researcher
The outcome measured in the study
Random variation in participant responses
A variable that confounds the results
The independent variable is the condition that researchers systematically manipulate to observe its effect on the dependent variable. It is not the outcome measure, which is the dependent variable. See for more details.
What is the dependent variable in a psychological experiment?
Random assignment of participants
The condition manipulated by the researcher
The outcome measured in the study
A variable that introduces bias
The dependent variable is the outcome that researchers measure to see if it changes in response to manipulations of the independent variable. It represents the effect in a cause-and-effect design. More information at .
What is a confounding variable?
The main variable being manipulated
Random measurement error
An outside factor that varies systematically with the independent variable
The primary outcome measured
A confounding variable is an extraneous factor that changes along with the independent variable and can provide an alternative explanation for the results. It compromises internal validity if not controlled. For more, see .
What is an operational definition in research?
A random assignment method
A statistical analysis plan
The theoretical concept behind a variable
A description of how variables are measured or manipulated
An operational definition specifies the exact procedures used to measure or manipulate a variable in a study. It bridges abstract concepts and observable measures. Read more at .
Which level of measurement categorizes data without any order?
Nominal
Ratio
Ordinal
Interval
Nominal measurement assigns labels to categorize data without any quantitative value or order. It is the simplest level of measurement. For details, see .
Which level of measurement ranks data but does not assume equal intervals between ranks?
Ordinal
Ratio
Nominal
Interval
Ordinal measurement involves ordered categories where the order matters, but the distances between ranks are not equal. Examples include Likert scale data. More information at .
Which measurement level has equal intervals but lacks a true zero point?
Ratio
Ordinal
Interval
Nominal
Interval scales have equal spacing between values but no true zero, so ratios are not meaningful. Temperature in Celsius is a classic example. See for more.
Which measurement level has equal intervals and a true zero point?
Ordinal
Ratio
Nominal
Interval
Ratio scales have consistent intervals and an absolute zero, allowing for meaningful ratios (e.g., weight, height). Zero indicates absence of the attribute. Learn more at .
What is random error in measurement?
Consistent bias in measurement
Error due to experimental manipulation
Variation in scores due to chance fluctuations
A type of systematic confound
Random error refers to unsystematic fluctuations in measurement due to chance or unpredictable factors. It affects reliability but not validity. More at .
What is systematic error in measurement?
Variability due to participant mood
Consistent bias that skews all measurements
Random sampling error
Chance fluctuations in scores
Systematic error is bias that consistently pushes measurements away from the true value, impacting validity. It does not even out over time like random error. See for more.
What does internal validity refer to?
The appropriateness of measurement tools
How well results generalize to other settings
The extent to which results are due to the manipulated variables
The consistency of scores over time
Internal validity assesses whether observed effects are attributable to the independent variable rather than confounds. It is critical for establishing cause-and-effect. Read .
What does external validity refer to?
The generalizability of findings to other contexts
The consistency of the results
The appropriateness of the sample size
The accuracy of the measurement instrument
External validity is about whether study results apply to contexts outside the research setting, such as different populations or environments. It is vital for real-world applicability. More at .
What does construct validity assess?
The test's consistency over time
How well results can be generalized
The absence of measurement bias
Whether a test measures the theoretical construct it claims to measure
Construct validity examines if a test truly assesses the theoretical concept it intends to measure. It involves both convergent and discriminant validity evidence. See .
What is face validity?
The extent to which a measure appears effective on its surface
The statistical accuracy of a measure
How well it predicts future outcomes
The measure's consistency across forms
Face validity refers to whether a measure seems valid at face value, based on subjective judgment. It is a superficial form of validity and not sufficient alone. More at .
What does criterion validity evaluate?
Whether the measure looks valid
How well a measure correlates with a relevant outcome
The consistency of the measure
The theoretical foundation of the measure
Criterion validity examines how well one measure predicts an outcome based on another established criterion. It includes both concurrent and predictive validity. See .
What is reliability in research measurement?
The level of precision in an experimental manipulation
The accuracy of what the test intends to measure
The generalizability to other contexts
The consistency of a measure across time or items
Reliability refers to the extent to which a measure yields consistent results over time or across items. It is different from validity, which concerns accuracy. More information at .
Which sampling technique ensures that subgroups of a population are represented proportionally?
Convenience sampling
Simple random sampling
Snowball sampling
Stratified random sampling
Stratified random sampling divides the population into subgroups and randomly selects from each group proportionally, ensuring representation. It reduces sampling bias across key characteristics. See .
What is a Type I error in hypothesis testing?
Accepting both null and alternative hypotheses
Failing to reject a false null hypothesis
Rejecting a true null hypothesis
Mistaking a confound for an independent variable
A Type I error occurs when the researcher incorrectly rejects a true null hypothesis, indicating a false positive. It is controlled by the alpha level (e.g., p<.05). More details at .
What is a Type II error in statistical testing?
Accepting a confounding variable
Using a nonparametric test incorrectly
Failing to reject a false null hypothesis
Rejecting a true null hypothesis
A Type II error happens when the researcher fails to reject a false null hypothesis, leading to a false negative. It is related to statistical power. For more, see .
What does a p-value represent in hypothesis testing?
The probability that the alternative hypothesis is true
The effect size in the population
The proportion of total variance explained
The probability of observing data as extreme given the null hypothesis is true
A p-value indicates the probability of obtaining results at least as extreme as those observed, assuming the null hypothesis is true. It does not give the probability of the hypothesis itself. More at .
What does the correlation coefficient (r) measure?
The reliability of a measure
The difference between group means
The amount of variance in one variable
The strength and direction of a linear relationship between two variables
The correlation coefficient (r) quantifies both the strength and direction of linear association between two continuous variables, ranging from -1 to +1. It does not imply causation. See .
Which statement best describes a null hypothesis?
A theory explaining the results
A specific directional prediction
An alternative outcome measure
A statement predicting no effect or difference
The null hypothesis posits that there is no effect or difference between groups or conditions. It serves as the default assumption in statistical testing. Learn more at .
What characterizes a directional hypothesis?
It describes a confound's role
It is non-testable
It predicts the specific direction of an effect
It predicts no difference
A directional hypothesis specifies whether the effect is expected to increase or decrease relative to the control or comparison condition. It is also called a one-tailed hypothesis. See .
What is a non-directional hypothesis?
It states no expected difference
It predicts an effect but not its direction
It predicts a negative effect only
It predicts a positive effect only
A non-directional hypothesis predicts that there will be a difference or relationship but does not state whether it will be positive or negative. It is also known as a two-tailed hypothesis. More at .
Which research design uses the same participants in all experimental conditions?
Between-subjects design
Within-subjects design
Case study design
Cross-sectional design
Within-subjects designs have participants experience every level of the independent variable, reducing error variance due to individual differences. However, they can be subject to carryover effects. Learn more at .
What is a between-subjects design?
A mixed-method research approach
A design using the same participants in all conditions
A design where different participants are in each condition
A correlational survey method
Between-subjects designs assign different participants to each level of the independent variable, avoiding carryover effects but requiring larger sample sizes. It enhances independence of observations. See .
What is the placebo effect?
Participants' improvement due to belief in treatment
An error in data recording
Random assignment failure
Bias introduced by researchers
The placebo effect occurs when participants experience a perceived or actual improvement in their condition because they believe they are receiving an effective treatment. It highlights the power of expectations. Read .
What are demand characteristics?
Confounding variables in experiments
Random errors in measurement
Cues that lead participants to guess study aims
Statistical artifacts in data
Demand characteristics are subtle cues in an experiment that can influence participants to behave in a way that confirms the researcher's expectations. They threaten internal validity. Learn more at .
What is a double-blind procedure?
Researchers manipulate two variables simultaneously
Participants know their condition but researchers do not
Neither participants nor researchers know condition assignments
A study with two stages
In a double-blind procedure, both the participants and the experimenters are unaware of condition assignments to prevent bias. It enhances internal validity by controlling expectancy effects. See .
What is the main purpose of an Institutional Review Board (IRB)?
To recruit participants for experiments
To protect the rights and welfare of research participants
To fund research projects
To publish academic findings
The IRB reviews research proposals to ensure ethical standards are met and participants' rights are protected. It evaluates risks, consent forms, and confidentiality measures. More at .
In ANOVA, what does the F-ratio represent?
The ratio of systematic variance to unsystematic variance
The median variance of the sample
The correlation between variables
The difference between two means
The F-ratio in ANOVA compares variance explained by group differences (systematic variance) to variance due to random error (unsystematic variance). A larger F indicates stronger effect of the independent variable. See .
Which assumption is essential for parametric statistical tests?
Equality of sample sizes only
Nominal level of measurement
Presence of outliers
Normal distribution of the residuals
Parametric tests typically assume that the residuals or data are normally distributed in the population. This underpins accurate significance testing. More at .
What is homogeneity of variance?
Equal variances across groups or conditions
Normal distribution of variables
Zero variance within the data
Identical means across samples
Homogeneity of variance means that different groups have similar levels of variability, an assumption for ANOVA and other parametric tests. Violation can affect Type I error rates. Learn more at .
What is a mediator variable in research?
A variable that influences the strength of a relationship
A variable that explains the relationship between independent and dependent variables
A measure of data dispersion
An extraneous variable causing bias
A mediator variable accounts for the mechanism through which an independent variable influences a dependent variable, indicating a causal chain. It helps explain how effects occur. For details see .
What is a moderator variable?
A variable that affects the strength or direction of a relationship
A type of experimental control
An irrelevant extraneous factor
A variable that mediates the causal pathway
A moderator variable alters the strength or direction of the relationship between an independent and dependent variable, indicating under what conditions effects occur. It is not on the causal path. Learn more at .
What is multicollinearity in multiple regression?
A form of measurement error
The overall model R-squared
Low correlation between predictors and outcome
High correlation among predictor variables
Multicollinearity occurs when two or more predictors in regression are highly correlated, making it difficult to isolate their individual effects. It inflates standard errors and reduces statistical power. See .
How is effect size represented in Cohen's d?
Difference between two means divided by the pooled standard deviation
Correlation coefficient squared
Square root of explained variance
Sum of squared deviations
Cohen's d quantifies the standardized difference between two group means by dividing that difference by the pooled standard deviation. It is a common measure of effect size. More at .
What is statistical power?
The alpha level of a test
The effect size squared
The probability of correctly rejecting a false null hypothesis
The probability of making a Type I error
Statistical power is the likelihood that a test will detect an actual effect (i.e., reject a false null hypothesis). It is influenced by effect size, alpha level, and sample size. Read .
How does increasing sample size affect statistical power?
It only affects Type I error
It has no effect on power
It decreases power by adding variability
It increases power by reducing standard error
Larger sample sizes decrease the standard error, making it easier to detect true effects, thereby increasing statistical power. It also reduces the likelihood of Type II errors. More at .
What is the key difference between experimental and correlational research?
Correlational research establishes causation, experimental does not
Experimental research involves manipulation of variables, while correlational research does not
Correlational research uses random assignment
Experimental research only uses surveys
Experimental research manipulates an independent variable to observe causal effects, while correlational research measures variables without manipulation to assess relationships. Correlation does not imply causation. See .
What does a partial correlation assess?
The squared multiple correlation
The interaction effect in regression
The relationship between two variables controlling for a third variable
The correlation after data normalization
A partial correlation measures the association between two variables while statistically controlling for the influence of one or more additional variables. It isolates unique variance. More at .
What is MANOVA used to analyze?
Factor structure of variables
Correlations among multiple variables
A single dependent variable across groups
Differences on multiple dependent variables across groups
Multivariate analysis of variance (MANOVA) examines group differences on two or more dependent variables simultaneously, accounting for intercorrelations. It reduces Type I error compared to multiple ANOVAs. Learn more at .
What is a prospective longitudinal study?
Examining data at a single point in time
Comparing historical records with current data
Following the same participants and measuring outcomes over time
Random assignment of participants to groups
Prospective longitudinal studies track the same participants and record data at multiple time points, enabling observation of changes over time. They help establish temporal sequence. More at .
What defines qualitative research methods?
Collecting non-numerical data to understand concepts and experiences
Using only statistical tests
Manipulating variables experimentally
Measuring interval data exclusively
Qualitative research gathers narrative or observational data to explore subjective experiences, meanings, and social processes. It often uses interviews, focus groups, or thematic analysis. Learn more at .
What is thematic analysis in qualitative research?
Coding data for quantitative analysis only
Recording data at multiple time points
Identifying and analyzing patterns or themes in qualitative data
A statistical comparison of group means
Thematic analysis involves systematically coding qualitative data to identify recurring themes and patterns that provide insight into participants' experiences. It is widely used in psychology. See .
In structural equation modeling (SEM), what does a path coefficient represent?
The correlation between observed variables only
The error variance of a manifest variable
The strength and direction of the relationship between latent variables
The sample size required for model fit
In SEM, a path coefficient quantifies the magnitude and direction of the hypothesized effect between latent (or observed) variables. It functions like a regression weight in a causal model. For more, see .
What is measurement invariance in psychometric testing?
The ability to predict external criteria
High internal consistency of a scale
The property that a measure operates equivalently across different groups
The uniqueness of each test item
Measurement invariance means that a scale measures the same construct in the same way across different groups (e.g., genders, cultures). It is tested through multi-group confirmatory factor analysis. See .
In item response theory (IRT), what does the difficulty parameter (b) indicate?
The discrimination between items
The point on the trait continuum where the item has a 50% chance of being answered correctly
The guessing probability of an item
The total score variance
The IRT difficulty parameter specifies the latent trait level where a respondent has a 50% probability of endorsing or correctly answering the item. It locates items along the ability continuum. More at .
What is a latent variable in statistical modeling?
Random measurement noise
An unobserved construct inferred from measured variables
A confounding variable not controlled
A variable directly measured through observation
Latent variables represent underlying theoretical constructs that are not directly observed but are inferred through observed indicators. They are common in factor analysis and SEM. Learn more at .
What distinguishes fixed effects from random effects in mixed-effects models?
Random effects are tested at alpha = .05, fixed at .01
Fixed effects vary by subject, random effects are constant
Fixed effects require larger samples than random effects
Fixed effects estimate average relationships, random effects account for individual variability
In mixed-effects models, fixed effects represent population-level average relationships, whereas random effects capture variability in intercepts or slopes across units (e.g., subjects). They allow for hierarchical data. More details at .
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Study Outcomes

  1. Differentiate Variable Types -

    Explain and distinguish between independent, dependent, and control variables to strengthen your foundational knowledge for psy exam 1 and beyond.

  2. Classify Measurement Levels -

    Identify nominal, ordinal, interval, and ratio scales to accurately measure and interpret psychological data in research methods quiz scenarios.

  3. Analyze Error Types -

    Recognize systematic and random errors, assess their impact, and implement strategies to minimize measurement inaccuracies in experimental settings.

  4. Evaluate Validity -

    Assess content, construct, and external validity to ensure that your research designs yield credible and generalizable results in psychology exam practice.

  5. Formulate Operational Definitions -

    Create clear, testable definitions for psychological constructs, enabling consistent measurement and replication in research studies.

  6. Apply Experimental Design Principles -

    Design basic experiments, select appropriate control procedures, and implement randomization techniques to conduct robust psychological research.

Cheat Sheet

  1. Key Variables: IVs, DVs & Controls -

    Grasping the roles of independent (IV) and dependent variables (DV) is crucial for any experiment; for example, in a drug trial the IV might be dosage while the DV is symptom reduction (University of North Carolina). Control variables - like age or time of day - help you isolate true cause-and-effect (APA).

  2. Measurement Levels (NOIR) -

    Use the mnemonic "NOIR" (Nominal, Ordinal, Interval, Ratio) to remember your scales: nominal labels categories, ordinal ranks them, interval has equal spacing (e.g., temperature in °C), and ratio adds a true zero point (e.g., weight in kg) (Stevens, 1946; University of Michigan).

  3. Operational Definitions -

    Translate abstract concepts into measurable terms - if you're measuring "stress," specify it as "salivary cortisol levels in μg/dL" or "scores on the Perceived Stress Scale" to ensure clarity and replicability (APA Research Methods).

  4. Validity: Internal vs. External -

    Internal validity ensures your IV really causes DV changes (control confounds!); external validity gauges how well findings generalize beyond your sample - balancing both is a core focus of Shadish, Cook & Campbell's framework (Campbell & Stanley, 1963).

  5. Error Types: Type I & II -

    Know your alpha (Type I error, false positive, e.g., claiming an effect at p < .05 when none exists) and beta (Type II error, false negative, missing a real effect); using proper sample size formulas (n = [(Zα/2+Zβ)σ/Δ]²) can help optimize your power (Field, 2013).

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