Questions and Answers
What is the Mahalanobis distance an extension of?
What does the Mahalanobis distance give?
What is the purpose of checking the Mahalanobis distance against cut-offs based on the Chi square distribution?
What type of outliers have high leverage but low influence?
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What is the assumption being tested by detecting multivariate outliers?
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What question should be asked when dealing with outliers?
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What can be done to reduce the intercorrelation of main effects with their interaction terms in a model?
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What is the primary goal of the weekly workshop/Q&A session?
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When can you not worry about multicollinearity in a model?
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Why is it important to know about the assumptions underlying statistical tests?
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What is a common issue with statistics students?
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What is the purpose of working through difficult quiz questions in the workshop/Q&A session?
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Which assumption is perhaps the most important to be careful of in regression-based analyses?
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What is the main goal of Exploratory Factor Analysis (EFA)?
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What is the focus of the one 2-hour tutorial per week?
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What is the purpose of the tutorial allocation system?
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What should be done before conducting an Exploratory Factor Analysis (EFA)?
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What is the benefit of attending the workshop/Q&A session?
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What is the main problem with using researcher degrees of freedom to p-hack?
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What should be done when dealing with assumptions in statistical tests?
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What is the prerequisite for this course?
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What is the format of the lecture material?
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What is the primary goal of adjusting factor loadings in the model?
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What constrains the values that can be predicted by the model?
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What is generated by the pattern of factor loadings?
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What is the relationship between the predicted and empirical variance-covariance matrices?
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What is the impact of the simplifying assumptions made by the model on our description of the data?
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What is the purpose of estimating model parameters?
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What is the primary goal of Confirmatory Factor Analysis?
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What does the factor structure in Confirmatory Factor Analysis represent?
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What is the role of the V(E) variable in the model?
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What is the purpose of the C(E,O) variable in the model?
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What does the Confirmatory Factor Analysis model evaluate?
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What is the goal of evaluating the model's ability to recover the data?
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What is the relationship between the variables E, O, So, W, T, and Sy in the model?
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What is the purpose of the C(E,W) variable in the model?
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Study Notes
Course Structure
- The course consists of one recorded lecture per week, available beforehand, covering theory, examples, and applications.
- Weekly workshop/Q&A sessions focus on clarifying and deepening understanding of the lecture material.
- One 2-hour tutorial per week covers practical details involved in conducting analysis in SPSS.
Assumption: No Overly Influential Observations
- Detecting multivariate outliers using Mahalanobis distance, a multivariate extension of univariate distance from mean.
- Check against cut-offs based on Chi square distribution (p < 1: very influential, may be problematic).
- High leverage, low influence outliers can be problematic.
- Ways to address multivariate outliers:
- Go back and look if any variables can be combined (or deleted).
- Factor analyze the set of independent variables.
- Centre or standardize independent variables.
- Collect more data to increase precision of betas.
Assumptions Testing
- Importance of knowing assumptions underlying statistical tests.
- Real-life data rarely conform precisely to assumptions, so informed judgement is needed about how problematic violations are.
- Regression-based analyses are generally robust to distributional assumptions.
- Independence of observations is crucial to be careful of.
- Being knowledgeable about assumptions helps navigate decisions.
- Transparency is key: don't use researcher degrees of freedom to p-hack.
Exploratory Factor Analysis (EFA)
- Conceptual introduction to EFA: aims of the analysis and research questions.
- How EFA works: preparing to conduct an EFA, determining how many factors to retain.
- Evaluating the model: how accurately can the model recover the data?
Confirmatory Factor Analysis (CFA)
- How CFA works: evaluating how well the hypothesized factor structure accounts for data.
- Model parameters estimation: pattern of factor loadings generates predictions about the values of items in the predicted variance-covariance matrix.
- Adjusting factor loadings to maximize similarity between predicted and empirical values.
- Hypothesized factor structure constrains values that can be predicted by the model.
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Description
This course covers the theory and practical applications of data analysis using SPSS. It includes lectures, workshops, and tutorials to help students understand and conduct analysis in SPSS.