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Questions and Answers
What are the four basic elements contained in an experimental design?
How is a quasi-experimental design different from an experimental design?
Which of the following problems in establishing causation relates to changes in one variable that are accompanied by changes in many other variables?
Which method involves ensuring that a specific variable remains unchanged to address internal validity?
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What issue arises from the relation between depression and insomnia as described in the content?
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Which of the following is NOT a method of actively controlling for extraneous variables?
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What can occur when extraneous variables influence both the independent and dependent variables?
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In which type of design does a researcher examine changes in behavior related to age through different groups of individuals?
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What is the primary role of a systematic review in research?
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Which statistical test should be used for comparing the means of more than two groups?
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How does a meta-analysis differ from a traditional literature review?
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Which statistical test is appropriate for comparing two groups’ medians with ordinal-level data?
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In evidence-based practice (EBP), how should a patient's preference be evaluated?
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What is the purpose of conducting statistical calculations in a meta-analysis?
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Which test is suitable for comparing proportions in different categories?
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A researcher is evaluating two independent groups' means. Which statistical test should they employ?
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What is a key characteristic of a systematic review compared to a traditional literature review?
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If a study involves a dependent outcome variable, which statistical test would be appropriate?
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What characterizes a factorial design?
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Which of the following statements accurately describes an effect size?
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In a multiple-baseline design, what is a primary feature?
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What does a p-value less than 0.05 generally indicate?
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What is the primary purpose of inferential statistics?
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Which design involves observing the same subjects under different conditions?
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In the context of hypothesis testing, which statement defines the null hypothesis?
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What is a key ethical concern associated with the ABAB design?
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How is effect size commonly quantified?
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What is the relationship between internal and external validity in nonexperimental and correlational studies?
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What does a main effect refer to in a factorial design?
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What type of design is characterized by a series of treatments applied to the same subjects over different phases?
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What is one major limitation of single-case research studies?
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What is the main purpose of descriptive statistics?
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Study Notes
Experimental Designs
- Experimental designs have four basic elements: manipulation, measurement, comparison, and control.
- Nonexperimental and quasi-experimental designs do not manipulate independent variables, making them vulnerable to assignment bias.
- Assignment bias: occurs when participants in different groups have different characteristics that could influence the outcome of the study.
- The researcher does not create groups in nonexperimental and quasi-experimental designs, leading to assignment bias.
- True experiments utilize randomization to reduce assignment bias.
- Control: refers to the ability to eliminate or minimize the effects of extraneous variables that might influence the dependent variable.
Threats to Internal Validity
- Nonexperimental and quasi-experimental studies are always susceptible to threats to internal validity.
- Internal validity: the extent to which we can be confident that the independent variable caused the observed changes in the dependent variable.
- Two primary types of developmental research designs: cross-sectional and longitudinal.
- Cross-sectional design: compares different age groups at a single point in time,
- Longitudinal design: tracks the same group of participants over time.
Problems in Establishing Causation
- The third variable problem: when changes in one variable are accompanied by changes in other variables, making it difficult to determine the true cause of the effect.
- The directionality problem: when it is unclear which variable is causing the change in the other, even if a correlation exists.
- Controlling nature: strict controls are necessary to isolate the effects of the independent variable and establish causation.
Extraneous and Confounding Variables
- Extraneous variables: any variables that are not of primary interest but could potentially influence the dependent variable.
- When extraneous variables impact both the independent and dependent variables, they become confounding variables.
- Environmental variables: aspects of the physical environment that might affect the results of the study.
- Individual differences: characteristics of participants that vary and might influence the dependent variable.
- Time-related variables: changes that occur over time that might influence the dependent variable.
Actively Controlling for Extraneous Variables
- Three methods for actively controlling extraneous variables:
- Holding a variable constant: ensures that all participants experience the same level of a particular variable.
- Matching across treatment conditions: ensures that groups are similar on a specific variable.
- Control by randomization: ensures that participants are randomly assigned to different groups, minimizing the effects of extraneous variables.
Internal vs. External Validity
- Holding a variable constant strengthens internal validity but can limit external validity.
- Simulation experiments aim to increase external validity by replicating real-world situations, but they might have lower internal validity than controlled experiments.
- Field studies, conducted in natural settings, often have higher external validity but lower internal validity compared to laboratory experiments.
Factorial Designs
- Factorial design: a type of experiment that includes two or more independent variables or factors.
- Factors: independent variables that are manipulated in a factorial design.
- Main effect: the effect of a single factor on the dependent variable, averaged across the levels of the other factors.
- Interaction: occurs when the effect of one factor on the dependent variable depends on the level of the other factor.
Types of Experimental Designs
- Between-subjects design: participants are randomly assigned to different conditions.
- Within-subjects design or repeated measures design: each participant experiences all conditions of the independent variable.
- Mixed design: a combination of between-subjects and within-subjects designs.
Descriptive and Inferential Statistics
- Descriptive statistics: used to summarize and describe data.
- Inferential statistics: used to draw conclusions about a population based on a sample of data.
- Descriptive statistics: provide basic information about variables and highlight potential relationships between them.
- Inferential statistics: use sample information to make general conclusions about the population.
Null and Alternative Hypotheses
- Null hypothesis (H0): a statement about the population indicating no effect, no change, no difference, or no relationship.
- Alternative hypothesis (H1): the research hypothesis that there is an effect, change, difference, or relationship.
P-Values
- A p-value represents the probability of observing the data if the null hypothesis is true.
- p < 0.05: indicates a statistically significant result, suggesting that the observed data is unlikely to have occurred by chance if the null hypothesis were true.
Effect Size
- Effect size: a standardized measure of the magnitude of an observed effect, independent of sample size.
- Cohen's d: a common effect size statistic for the difference between means.
- Pearson's correlation coefficient (r): a common effect size statistic for the strength of a linear relationship.
Single-Case Research Studies
- Single-case research studies focus on a single participant or a small number of participants.
- To qualify as an experiment, single-case studies must involve:
- Manipulation of the independent variable.
- Strict control of extraneous variables.
- Visual inspection: evaluates the results of single-case studies using graphs to determine if the changes in the dependent variable are substantial enough to be considered a large effect.
ABAB Design
- ABAB design: a single-case design consisting of four phases: baseline (A), treatment (B), baseline (A), treatment (B).
- Ethical concern: it can be difficult to justify withdrawing effective treatment in the second baseline phase, especially if it impacts the participant's well-being.
Multiple-Baseline Design
- Multiple-baseline design: consists of a series of A-B designs stacked on top of one another, with each baseline phase progressively longer.
- The treatment is applied to different target variables, participants, or settings in a staggered fashion.
- Strength of the multiple-baseline design: it eliminates the need to withdraw treatment to demonstrate its effectiveness, which is particularly beneficial in settings where treatment withdrawal is not ethical or practical.
Statistical Tests
- Independent-samples t-test: compare means of two groups for between-subjects designs with a continuous dependent variable.
- Analysis of variance (ANOVA): compare means of more than two groups for between-subjects designs with a continuous dependent variable.
- Dependent samples t-test: compare means of two groups for within-subjects designs with a continuous dependent variable.
- Chi-square test: analyze categorical data to compare proportions between different groups.
- Mann-Whitney U test: compare medians of two groups with an ordinal dependent variable.
- Kruskal-Wallis test: compare medians of more than two groups with an ordinal dependent variable.
Systematic Reviews and Meta-Analyses
- Systematic reviews: summarize all available evidence on a topic by critically appraising studies, using a rigorous and standardized method for selection, data extraction, and analysis.
- Meta-analysis: statistically combines the results from multiple studies to generate a quantitative estimate of the overall effect.
- A primary difference between a systematic review and a meta-analysis: a systematic review summarizes evidence, while a meta-analysis combines data to calculate an overall effect.
Patient Preferences in EBP
- The ethical principles of autonomy and beneficence should guide clinical decision-making.
- A fully informed patient's preferences should be of primary importance.
- However, when multiple efficacious treatments are available, compelling evidence suggesting substantial superiority of one approach over others should be considered.
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Description
This quiz covers key concepts in experimental designs, including manipulation, measurement, and control. It also addresses the significance of internal validity and the threats posed by nonexperimental and quasi-experimental designs. Test your understanding of how assignment bias affects research outcomes.
