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Questions and Answers
How does random sampling enhance external validity?
How does random sampling enhance external validity?
Which of the following best defines conceptual replication?
Which of the following best defines conceptual replication?
What is a significant threat to external validity related to sample selection?
What is a significant threat to external validity related to sample selection?
Which strategy is recommended to address volunteer bias in research studies?
Which strategy is recommended to address volunteer bias in research studies?
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What technique can researchers use to address sample bias?
What technique can researchers use to address sample bias?
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What is a key advantage of using an ABAB design compared to an ABA design?
What is a key advantage of using an ABAB design compared to an ABA design?
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Which of the following describes a multiple baseline design?
Which of the following describes a multiple baseline design?
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How can threats to internal validity be addressed in experimental design?
How can threats to internal validity be addressed in experimental design?
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What does it mean when a study shows regression toward the mean?
What does it mean when a study shows regression toward the mean?
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What is a critical component of the multiple baseline design across different settings?
What is a critical component of the multiple baseline design across different settings?
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What is one potential effect of conducting a pretest in a one-group pretest-posttest design?
What is one potential effect of conducting a pretest in a one-group pretest-posttest design?
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In an ABAB design, why is it beneficial to end with the treatment phase?
In an ABAB design, why is it beneficial to end with the treatment phase?
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Which of the following is a history effect in experimental design?
Which of the following is a history effect in experimental design?
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What does a Type I error indicate?
What does a Type I error indicate?
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Which statement accurately defines the role of effect size in statistics?
Which statement accurately defines the role of effect size in statistics?
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In hypothesis testing, the null hypothesis generally suggests that:
In hypothesis testing, the null hypothesis generally suggests that:
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When is analysis of variance appropriately used?
When is analysis of variance appropriately used?
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What does systematic variance represent in a group context?
What does systematic variance represent in a group context?
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When should a t-test be used in statistical analysis?
When should a t-test be used in statistical analysis?
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What is the role of sampling distributions in hypothesis testing?
What is the role of sampling distributions in hypothesis testing?
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What does a Type II error involve?
What does a Type II error involve?
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What is the primary purpose of an F-test?
What is the primary purpose of an F-test?
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Which statement best differentiates between internal and external validity?
Which statement best differentiates between internal and external validity?
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Which of the following is an example of a null hypothesis?
Which of the following is an example of a null hypothesis?
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Which factor does NOT influence the generalizability of research findings?
Which factor does NOT influence the generalizability of research findings?
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How is inferential statistics primarily utilized in research?
How is inferential statistics primarily utilized in research?
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What is the purpose of exact replications in research?
What is the purpose of exact replications in research?
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How can volunteer characteristics introduce bias into research findings?
How can volunteer characteristics introduce bias into research findings?
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Why is replication important in research?
Why is replication important in research?
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What is a key characteristic of single-case experimental design?
What is a key characteristic of single-case experimental design?
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Which design includes observing behavior before and after manipulation in multiple circumstances?
Which design includes observing behavior before and after manipulation in multiple circumstances?
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How does a reversal design demonstrate the effect of an independent variable?
How does a reversal design demonstrate the effect of an independent variable?
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What is a feature of quasi-experimental designs?
What is a feature of quasi-experimental designs?
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Which of the following designs lacks a pretest and control group?
Which of the following designs lacks a pretest and control group?
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In which design are participants observed over time to assess the impact of an independent variable?
In which design are participants observed over time to assess the impact of an independent variable?
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What distinguishes the nonequivalent control group design from true experimental designs?
What distinguishes the nonequivalent control group design from true experimental designs?
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What is the primary purpose of an ABA design?
What is the primary purpose of an ABA design?
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What is a main limitation of one-group pretest-posttest designs?
What is a main limitation of one-group pretest-posttest designs?
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What distinguishes inferential statistics from descriptive statistics?
What distinguishes inferential statistics from descriptive statistics?
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What occurs when participants' behavior is influenced by taking a pretest?
What occurs when participants' behavior is influenced by taking a pretest?
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Which of the following correctly defines the null hypothesis?
Which of the following correctly defines the null hypothesis?
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Why are control groups important in experimental research?
Why are control groups important in experimental research?
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In hypothesis testing, if the research hypothesis is true, what can be inferred about the null hypothesis?
In hypothesis testing, if the research hypothesis is true, what can be inferred about the null hypothesis?
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How does probability relate to inferential statistics?
How does probability relate to inferential statistics?
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What might cause instrument decay in human observers during behavior measurement?
What might cause instrument decay in human observers during behavior measurement?
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Study Notes
Experimental Designs
- Understand the distinctions between experimental designs like single-case, control series, and others.
- Single-case designs allow cause-and-effect inferences from data on one or a few participants.
- Reversal designs introduce a treatment after a baseline, then withdraw it during a second baseline period. Demonstrating the reversibility of the treatment shows its effect.
- Multiple baseline designs observe behavior before and after manipulation across different contexts or participants.
- Quasi-experimental designs approximate true experimental controls to infer treatment effects.
- One-group posttest-only designs (one-shot case study) lack a control group and pre-test.
- One-group pretest-posttest designs measure behavior before and after intervention.
- Nonequivalent control group designs compare experimental groups with control groups, not randomly assigned.
- Nonequivalent control group pretest-posttest designs add pre- and post-tests.
- Interrupted time series designs track the dependent variable over time before and after treatment.
- Control series designs extend interrupted time series by including a comparison group.
- Single-case designs track a subject's behavior during baseline and treatment periods to identify treatment effects.
Reversal and ABA Designs
- Reversal designs and ABA designs demonstrate the reversibility of a treatment to show its effect.
- ABA designs observe a baseline (A), treatment (B), and a second baseline (A) period. A change from baseline to treatment and back to baseline shows that the treatment changed the behavior.
- Extending ABA to ABAB or ABABAB can confirm the treatment's impact.
Multiple Baseline Designs
- Multiple baseline designs examine behavior in different settings, behaviors, or participants, at different times. The change after treatment in any of the observations suggests that the treatment is causing the change in behaviour.
Threats to Internal Validity
- History effects: external events interfering with the treatment effect.
- Maturation effects: natural changes within participants that might be confused with treatment effects.
- Testing effects: pre-testing affecting behavior in the post-test.
- Instrument decay: changes in measurement tools across time.
- Regression toward the mean: extreme scores tending to become closer to the average.
History Effects
- External events between measurements can confound results if not considered.
Maturation Effects
- Systematically occurring changes over time. They affect how to interpret results.
Selection Differences
- Pre-existing differences between groups can skew experimental results.
Cohort Effects
- Differences between age groups in cross-sectional studies could be based on generational differences, not age alone.
Sequential Designs
- Combining longitudinal and cross-sectional methods for developmental research.
Instrument Decay
- Measurement standards can change, affecting results particularly with human observers.
Testing Effects
- Pretesting can alter participant behavior in subsequent measurements.
One-Group Pretest-Posttest Designs
- Lacks a control group, making it vulnerable to many internal validity threats.
Control Groups
- Control groups help isolate independent variable effects.
Inferential Statistics
- Inferential statistics determine if results would be consistent across multiple samples, allowing broader generalization. They contrast with descriptive statistics which merely summarize data.
Hypotheses in Testing
- Null hypothesis posits no effect of the independent variable.
- Research hypothesis suggests an effect of the independent variable.
Probability
- Probability assesses the likelihood of outcomes, crucial for inferential statistics.
Type I and Type II Errors
- Type I error is rejecting a true null hypothesis.
- Type II error is failing to reject a false null hypothesis.
Null Hypothesis Examples
- Null hypothesis examples are provided in various contexts.
Effect Size
- Effect size shows the magnitude of the result, beyond the simple statistical significance.
Statistical Tests
- Various statistical tests (T-tests, ANOVA, Chi-square, Pearson correlation) evaluate relationships and differences. The type of test used depends on the nature of the variables..
Systematic Variance
- Systematic variance measures the extent to which differences among group means are due to the independent variable.
Sampling Distributions
- Sampling distributions use probability to infer about populations. They are used to calculate likelihoods of experimental results.
Internal vs External Validity
- Internal validity concerns accuracy in measuring cause-and-effect relationships within a study.
- External validity examines generalizability, if the findings can apply to other groups or circumstances.
Generalizability
- Factors affecting generalizability include participants, methods, and context. This is important in concluding results.
Replication in Research
- Replication helps verify findings and avoids issues with generalizations. Results need to be consistent.
Exact Replications
- Exact replication is attempting to repeat the exact procedure to confirm results.
Volunteer Bias and Gender Generalization
- Volunteer biases and gender concerns affect results and limit generalizations.
Random Sampling
- Random sampling ensures a representative sample, minimizing bias, and improving generalizability.
Conceptual Replications
- Conceptual replications improve knowledge and understanding of an effect by exploring it through different operational definitions.
External Validity Threats
- External Validity threats include sampling bias, volunteer bias, and overgeneralizing. Strategies to overcome these are provided.
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Description
This quiz delves into various experimental designs, including single-case, control series, and quasi-experimental models. Learn how different designs allow for cause-and-effect inferences and how they vary in structure and application. Understanding these concepts is crucial for accurate data interpretation and research methodology.
