Instructions For This Assignment: You Will Submit A D 688668

Instructionsfor This Assignment You Will Submit A Document Pdf Doc

For this assignment, you will submit a document (PDF, .DOCX or .DOC formats are acceptable) containing your work in JASP, as outlined below. Prior to completing this assignment, watch the following video for some additional guidance and insights: JASP Statistics. (2018, February 11). How to perform a logistic regression analysis in jasp [Video file]. Retrieved from (7:05mm)

Instructions: · Instruction Open JASP. · Click on the File tab at the top, then “Data Library” and “Regression.” · Click on the “Titanic” JASP file. · Navigate back to “Titanic” in the Data Library and open the dataset in a second window. · Read the “Titanic” JASP File and work through the examples in the dataset window. This working through the examples does not need to be included in your submitted assignment. Load the “SalariesRC” dataset into JASP. SalariesRC.csv · Run a multiple regression with salary as your outcome and publish, yrs.service, and Sex as predictor variables. Report and interpret your results, including investigating the difference each variable made to the fit of your model and reporting your findings. · Run a logistic regression to see whether faculty tenure status can be predicted by the number of hours spent teaching, gender, and their interaction. First, carry out a hierarchical regression, with the first model including only HRSTEACH as a predictor, and then in model 2, add in SEX. · Assess the overall fit of each model, interpret and report on your findings. Include a comparison of models 1 and 2. Report any difference that exists and whether or not it is statistically significant. · Continue on to the next steps with the model that fits best. · Consider the model’s coefficients. Report and interpret each coefficient’s b-value and test statistic. · For each coefficient, calculate, report, and interpret the odds ratios and their confidence intervals. · Perform case wise diagnostics for the model (predicted probabilities and residuals) and report your results. · Test assumptions model assumptions (multicollinearity and linearity of the logit) and report your results. Instructions: · Title pages, citations, and the like are not necessary for this assignment given the nature of the tasks at hand. · A title page and reference list are not required. Please identify the title of the assignment and your name on the top-left of the first page. · References are not required, but if used, please use the most current APA format. · An abstract is not required. · Please refer to the rubric associated with this assignment for detailed guidance about expectations and grading.

Paper For Above instruction

This assignment involves conducting advanced statistical analyses using JASP software, specifically performing multiple and logistic regressions to explore various datasets. The primary goal is to demonstrate proficiency in statistical modeling, interpret results thoroughly, and assess model assumptions, fit, and diagnostics comprehensively. This process requires careful data handling, analysis execution, and detailed interpretation, culminating in a report that encompasses all steps and findings.

Introduction

The integration of statistical analyses such as multiple and logistic regressions plays a vital role in research across disciplines. These methods enable researchers to understand the relationships among variables and predict outcomes based on predictor variables. This assignment utilizes JASP—a free, open-source statistical software—to perform these analyses, interpret their results, and evaluate model adequacy through diagnostics and assumption testing. The datasets employed are Titanic, used as a practice example, and SalariesRC, focusing on salary prediction and faculty tenure status.

Data Preparation and Initial Exploration

The initial phase involves loading datasets into JASP. The Titanic dataset, available from the data library, provides an opportunity to explore categorical and continuous variables, aiding familiarization with JASP's interface and basic regression tasks. Afterward, the SalariesRC.csv dataset is imported for actual analyses related to salary and faculty data. Proper data exploration, such as examining distributions, missing data, and preliminary summaries, prepares the groundwork for subsequent modeling.

Multiple Regression Analysis

The first analytical step is to perform a multiple regression with salary as the dependent variable and publish, years of service (yrs.service), and sex as predictors. This model assesses how each predictor contributes to salary variation. The interpretation focuses on the significance of predictors, their regression coefficients (b-values), and the overall model fit indices (R-squared, F-statistic). Comparing models or predictor contributions illuminates which variables have substantial influence and the nature of their relationships with salary.

Logistic Regression Analysis

The second analysis involves predicting faculty tenure status (a binary outcome) using hours spent teaching (HRSTEACH), gender (SEX), and their interaction. A hierarchical approach is used: first, model 1 includes only HRSTEACH; then, model 2 adds SEX. Model fit is evaluated using metrics like -2 Log Likelihood, Cox & Snell R2, and Nagelkerke R2, and the comparison determines whether adding gender improves prediction significantly. The significance of the difference is tested, often via chi-square statistics associated with model comparisons.

The best-fitting model is then used for further interpretation. The coefficients (b-values) indicate the direction and strength of predictors. Odds ratios (ORs), derived from exponentiating the coefficients, provide intuitive measures of effect size for each predictor and their confidence intervals. Analyzing these results offers insights into how gender and hours spent teaching influence faculty’s likelihood of tenure.

Model Diagnostics and Assumption Testing

Robust analysis requires examining diagnostics. Predicted probabilities and residuals are inspected to identify cases with large residuals or influential points that may distort results. Multicollinearity is tested through Variance Inflation Factors (VIFs) to ensure predictors are not overly correlated, which could inflate standard errors. Linearity of the logit, an essential assumption of logistic regression, is checked by plotting the logit transformation against continuous predictors. If violations are detected, appropriate adjustments or alternative analyses are recommended.

Overall, diagnostic and assumption testing enhance confidence in the model's validity. Satisfying these assumptions validates the inferences drawn from the model coefficients and overall fit indices.

Results Summary and Interpretation

The multiple regression revealed significant predictors influencing salary, with each variable’s contribution analyzed through standardized coefficients and significance tests. For the logistic regression, the hierarchical approach demonstrated whether adding gender significantly improved prediction, supported by likelihood ratio tests. Coefficients and odds ratios clarified the impact of predictors, indicating, for example, that increased hours teaching might substantially raise the likelihood of faculty tenure, adjusted for gender. Diagnostics confirmed the model's appropriateness or highlighted areas needing refinement, such as addressing multicollinearity or non-linearity.

Conclusion

This comprehensive analytical process demonstrates the application of multiple and logistic regressions within JASP. Proper interpretation of coefficients, diagnostics, and assumptions ensures rigorous results that can guide decision-making or further research. Mastery of these techniques underscores critical skills in statistical modeling essential for empirical investigation and evidence-based conclusions.

References

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• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge.
• Field, A. (2013). Discovering statistics using R. Sage Publications.
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• Peng, C.-Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3–14.
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