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Questions and Answers
What does a significant change in R² indicate when moving from Block 1 to Block 2 in hierarchical regression?
What does a significant change in R² indicate when moving from Block 1 to Block 2 in hierarchical regression?
Why is hierarchical regression preferred over stepwise regression in research that is theory-driven?
Why is hierarchical regression preferred over stepwise regression in research that is theory-driven?
What does a negative beta coefficient (β) for stress in the regression output imply?
What does a negative beta coefficient (β) for stress in the regression output imply?
In the context of multicollinearity, what is a common effect on the regression model?
In the context of multicollinearity, what is a common effect on the regression model?
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What does the interaction term in moderation analysis represent?
What does the interaction term in moderation analysis represent?
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What does the study show regarding the relationship between study hours and test scores?
What does the study show regarding the relationship between study hours and test scores?
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Which of the following statements is true about the variance explained in test scores by study hours?
Which of the following statements is true about the variance explained in test scores by study hours?
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How is the significance of the relationship between study hours and test scores indicated?
How is the significance of the relationship between study hours and test scores indicated?
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In the context of regression analysis, what does a regression coefficient indicate?
In the context of regression analysis, what does a regression coefficient indicate?
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What is a potential issue with selection bias in time series designs?
What is a potential issue with selection bias in time series designs?
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Which type of design is characterized by observing the same subjects multiple times over a period?
Which type of design is characterized by observing the same subjects multiple times over a period?
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What is the effect of additional hours studied on test scores as reported?
What is the effect of additional hours studied on test scores as reported?
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Which statistical concept evaluates the predictive power of an independent variable in regression?
Which statistical concept evaluates the predictive power of an independent variable in regression?
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Study Notes
Time Series Designs
- A research method using repeated measurements over time to observe changes due to an intervention or event.
- Pros: Tracks changes over time, useful for natural experiments/policy changes, reduces threats to internal validity (when well-designed).
- Cons: Sensitive to external confounding events, requires consistent reliable data collection, may struggle with causal inference without proper controls.
Comparison Groups
- Adding a comparison group improves causal inference by highlighting changes unrelated to the intervention.
- A comparison group always helps to increase internal validity of the design.
- Propensity score matching: A statistical technique to match individuals in the treatment and comparison groups.
Selection Bias
- Occurs when groups differ systematically, causing outcomes to be attributed to pre-existing differences instead of the intervention.
Simple Interrupted Time Series Design
- Measures outcomes before and after an intervention/event.
- Pros: Simple, intuitive, shows clear trends.
- Cons: No control for external factors, changes might not be due to intervention.
Interrupted Time Series Design with Reversal
- Measures outcomes before, during, and after an intervention reversal (reintroducing the policy).
- Pros: Demonstrates causality by observing if reversal undoes effects.
- Cons: Reversals may not always occur or be feasible.
Interrupted Time Series Design with Multiple Replications
- Repeatedly introduces and removes an intervention to observe consistent effects.
- Pros: Strong causal inference.
- Cons: Requires careful timing and replication.
Comparison Group Interrupted Time Series Design
- Adds a non-treated comparison group to observe natural changes versus intervention-induced changes.
- Pros: Controls for external factors affecting both groups.
- Cons: Requires well-matched comparison groups.
Regression Discontinuity Design
- Assigns treatment based on a cutoff score, examining outcomes above and below the cutoff.
- Pros: High internal validity for causal inference, doesn't require random assignment
- Cons: Only works near the cutoff (limited generalizability), requires a sharp cutoff and reliable assignment mechanism.
Correlational Designs
- Examines the relationship between two or more variables without manipulation.
- Correlation does not equal causation.
Correlation Coefficients
- Pearson's correlation coefficient (r) measures the strength and direction of a linear relationship between two variables.
- Magnitude: Values range from -1 to 1, indicating strength of association
- Direction: Positive (r > 0) or negative (r < 0)
- Significance: P-value less than 0.05 indicates statistical significance.
- r^2 (Coefficient of Determination): Proportion of variance in one variable explained by the other.
Simple Linear Regression
- Summarizes how predictor variables are related to the outcome variable.
- Allows prediction of the outcome based on predictor variables.
- Assumes a linear relationship between variables.
Multiple Regression
- Predicts a dependent variable using multiple independent variables.
- This is useful for including multiple predictors to better predict an outcome
- Includes aspects like: Standard (simultaneous), stepwise, hierarchical.
Hierarchical Regression
- Enters predictors into blocks based on theoretical or research priorities
- Aims to test if new predictors add unique variance beyond covariates or earlier blocks.
- Key statistics for interpretation include R^2 change, Regression F-test, unstandardized/standardized coefficients (b/Beta), and significance.
Moderation
- Occurs when the relationship between a predictor (X) and an outcome (Y) changes depending on the level of a third variable (moderator, M).
- Implies that the relationship between two variables is different at different levels of a third variable
- Indicates whether the effect of one variable on another depends on the level of a third variable
- Used to model interaction effects.
Mediation
- Occurs when the relationship between a predictor (X) and an outcome (Y) is explained (fully or partially) by an intermediary variable (mediator, M).
- Suggests that the effect of a predictor is exerted through a mediator
- Used to model the process by which a predictor influences an outcome.
Setting Level Research
- Examines the influence of environments/contexts on outcomes.
- Focuses on how characteristics of a setting shape individual or group outcomes.
- Types of settings include proximal factors (influences close to individuals), distal factors (broader influences).
- Tools used in setting-level research include cluster-randomized control trials & correlational research.
- Multilevel modeling is used to analyze data involving nested structures (e.g., students within schools).
Community-Engaged Research
- Philosophy that is rooted in community expertise and collaborative practices
- Types of approaches include community-based participatory research (CBPR) & participatory action research (PAR), which emphasises co-creation of knowledge, and mutual benefit.
- Approaches may include community involvement in data collection, analysis, and recommendations, ensuring the project is culturally relevant and sustainable while respecting the communities ethical practices and principles.
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Description
Explore the key concepts and techniques related to time series designs in research methods. This quiz covers the advantages and disadvantages of using repeated measurements over time, the importance of comparison groups, and the issue of selection bias. Test your understanding of how these elements influence causal inference and internal validity in research.
