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Questions and Answers
Which research design involves measuring two variables at the same moment in time?
What is a major limitation of correlational research designs?
In the context of experimental designs, what does random assignment help to control?
Which statement best describes an interrupted time-series design?
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What does the term 'spuriousness' refer to in research design?
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Which component is NOT typically found in a classical experiment?
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What is a fundamental characteristic of a double-blind experiment?
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What does standard research design notation 'R, N, X, O' refer to?
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What is the primary purpose of constructing clear tables in research?
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When the original bivariate relationship disappears after adding a test variable, what is this phenomenon called?
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Which aspect of a hypothesis is primarily confirmed by an association?
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What does a causal diagram illustrate?
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What is an essential requirement for probability sampling?
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What is a consequence of sampling error?
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What is the role of a response rate in research sampling?
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What characteristic differentiates non-probability sampling from probability sampling?
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What is the purpose of a sampling frame?
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Which type of sampling is characterized by a significant bias?
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What does the response rate in sampling describe?
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What is the primary distinction between non-probability and probability sampling?
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How can sampling error best be defined?
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Which sampling method involves asking an existing participant to recommend other participants?
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What factors are most critical to ensure when analyzing the relationship between sample size and significance?
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Which of the following is not considered a sampling bias?
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Study Notes
Research Designs for Causal Relationships
- There are three research designs to test causal relationships: correlational (cross-sectional), interrupted time-series, and (classical) experiments.
Cross-Sectional Research
- Cross-sectional research involves measuring variables at the same moment.
- Requires a unit and two variables.
- Cannot confirm time order because variables are measured simultaneously.
- Cannot control for a third variable, so it's difficult to establish causality.
- Establishes correlation, but not necessarily causation.
Interrupted Time-Series Research
- Measures a dependent variable (Y) before and after an independent variable (X) is changed.
- Usually involves multiple measurements over time.
- Can be seen as a more robust form of a before-after study.
- It's less effective than classical experiments in controlling for third variables, potentially leading to spurious relationships.
Classical Experiments
- Control the time order of the variables.
- Use random assignment of participants to treatment groups and control groups.
- Can be used to test causal relationships with a high level of certainty because the research design allows for a direct comparison of treatment and control groups.
- Minimizes threats to internal validity.
- Have high internal validity, making them a powerful tool for testing causal relationships.
Internal Validity
- Concerns the degree to which the effect of the independent variable on the dependent variable can be measured.
- Threats to internal validity arise from confounding variables that can produce a spurious relationship.
Features of a Classical Experiment
- Random Assignment: Participants are randomly assigned to treatment or control groups.
- Posttest: The outcome variable is measured after the treatment is administered.
- Pretest: The outcome variable is measured before the treatment is administered.
- Treatment: The intervention or manipulation that is being tested.
- Placebo: A control condition in some experiments that resembles the treatment but has no effect.
- Observation: The measure of the outcome variable at the posttest and sometimes pretest.
Double-Blind Experiment
- Neither the researcher nor the participant knows who is assigned to the treatment or control groups.
- Eliminates bias in data interpretation by minimizing the influence of researcher and participant expectations.
Experiment Notation
- R: Random assignment to groups.
- N: No random assignment.
- X: Treatment.
- O: Observation.
Confirming a Hypothesis with Tables
- Testing a hypothesis partially involves examining the relationship between variables.
- A trivariate table can be used to examine a trivariate hypothesis (2x2x2 table).
- Confirms the association aspect of the hypothesis.
- To some extent, can help control for third variable effects.
- Cannot confirm the time order component of the hypothesis.
Replication & Addition Models in Causality
- Help understand causality by examining how a relationship changes when additional variables are introduced.
- If a relationship exists but disappears after adding a third variable, it suggests spuriousness.
Multivariate Relationships
- Can be displayed using graphs and tables.
- Provide a comprehensive visual understanding of the relationships between multiple variables.
Sampling Methods
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Non-probability Sampling
- Convenience sampling: Sampling based on ease of access.
- Purposive sampling: Selecting participants based on specific characteristics.
- Snowball sampling: Recruiting participants through existing participants.
- Quota sampling: Matching the sample to the population in terms of demographics.
-
Probability Sampling
- Simple random sampling: Each unit in the sampling frame has an equal chance of selection.
- Systematic sampling: Selecting participants based on a regular interval within the sampling frame.
- Stratified sampling: Dividing the sampling frame into groups based on common characteristics and then randomly sampling from each group.
- Multistage cluster sampling: Randomly sampling groups and then randomly sampling units within those groups.
Sampling Errors and Bias
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Sampling Error
- Difference between the sample findings and the actual population values.
- Resulting from random variation in selection and sample size.
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Sampling Bias
- Systematic errors in the selection of the sample, leading to inaccurate representation of the population.
- Occurs when the sample is not representative of the population.
-
Non-response
- Occur when people refuse to participate in the study, leading to a lack of information.
- Can introduce bias and reduce the accuracy of results.
Response Rate
- The percentage of the sample that participated in the study after being selected.
- Calculated by dividing the number of participants by the total number of selected participants.
- A lower response rate can suggest bias in the study.
Effect Size, Significance, and Sample Size
- Effect size: Indicates the magnitude of the relationship between variables.
- Significance: Indicates if the observed effect is unlikely to have occurred by chance.
- Sample size: The number of participants in the study.
- Relationship: Larger effect sizes are typically associated with greater significance, but the effect of sample size can also influence significance.
- Larger samples can increase statistical power and the likelihood of finding a significant effect.
Calculating the Difference Between Percentages
- Can be used to measure the effect of one variable on another in a contingency table.
- Calculate the differences between column or row percentages to assess potential effects.
- Provide useful information about the strength and direction of the relationship.
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Description
Explore the three research designs used to test causal relationships: correlational (cross-sectional), interrupted time-series, and classical experiments. Understand the strengths and limitations of each design, especially in establishing causality versus correlation.
