PSYC3700 Week 8 Assignment
For this assignment you will use the Week_08_Assignment_Data.csv file, which can be found on the Week 8 Assignment page in Canvas. This is a hypothetical dataset. Assume these data were collected from a representative sample of students enrolled in one large introductory statistics course. Prior to taking a quiz on data visualization students complete a survey measuring their self-efficacy as it relates to data visualization. A researcher wants to use students’ self-efficacy scores to predict their quiz scores.
The dataset includes the following variables:
- id: A unique nominal-level identification number assigned to each student
- quiz_score: Score on a quiz measuring knowledge of data visualization
- self_efficacy: Composite score on a measure of self-efficacy as it relates to data visualization
Criteria:
- Construct a scatterplot in JASP
- Construct a plot of residuals versus predicted values in JASP
- Construct a graph in JASP that can be used to evaluate the normality of residuals
- Evaluate graphs to determine if the assumptions of simple linear regression have been met
- Construct a simple linear regression model using JASP
- Interpret the statistical significance of a simple linear regression model
- Write one sentence in APA style summarizing the results of the simple linear regression model
| In JASP, construct a scatterplot with self_efficacy on the x-axis and quiz_score on the y-axis. |
| [Include a screenshot of your scatterplot here] |
| Construct a plot of residuals versus predicted values for a regression model that uses self_efficacy to predict quiz_score. |
| [Include a screenshot of your graph here] |
| Construct a histogram or QQ plot of residuals for a regression model that uses self_efficacy to predict quiz_score. |
| [Include a screenshot of your graph here] |
| Explain how you checked each of the following assumptions of simple linear regression using the three graphs you have constructed. Explicitly state which graph you used to check each assumption and clearly describe what you looked for in that graph. |
| Linearity –
Independence of errors – Normality of residuals – Equal error variances – |
| Use JASP to construct a simple linear regression model that uses self_efficacy to predict quiz_score. |
| [Include screenshots of your Model Summary, ANOVA, and Coefficients tables here] |
| Is self_efficacy a statistically significant predictor of quiz_score? Explain how you determined this. |
| Write one sentence in APA style summarizing the results of your simple linear regression model. Include the following: test statistic (t or F), degrees of freedom, p value, R2 value. |
Overview
For this week’s assignment you will construct graphs, compute a correlation coefficient, and explain how the correlation can be used to support a specific type of reliability or validity.
Preparation
Before you begin this assignment, complete the following:
- Download the Week 8 Assignment Worksheet [DOCX].
- Download the csvfile.
Instructions
Complete and submit the Week 8 Assignment worksheet.
Competencies Measured
By successfully completing this assignment, you will demonstrate your proficiency in the following course competencies and rubric criteria:
- Competency 1: Communicate research findings using data visualizations.
- Evaluate graphs to determine if the assumptions of simple linear regression have been met.
- Competency 2: Interpret statistical findings in the context of their level of statistical and practical significance.
- Interpret the statistical significance of a simple linear regression model.
- Competency 3: Conduct statistical analyses to address research questions in the social sciences.
- Construct a scatterplot in JASP.
- Construct a plot of residuals versus predicted values in JASP.
- Construct a graph in JASP that can be used to evaluate the normality of residuals.
- Construct a simple linear regression model using JASP.
- Competency 7: Explain the results of statistical analyses in common language.
- Write one sentence in APA style summarizing the results of the simple linear regression model.
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