PSYC 3700 ASSIGN #4 Complete and submit the Assessment 4 worksheet.
Introduction
Note: The assessments in this course build upon each other. You are strongly encouraged to complete them in sequence.
In the fourth assessment, we will explore correlation and regression. These are often students’ favorite topics.
While you will be introduced to various correlation coefficients, we will focus primarily on Pearson’s r, which is the most commonly used correlation coefficient in psychological research. Pearson's r measures the strength and direction of the linear relationship between two interval- or ratio-level variables.
For example, imagine you're conducting a study to understand the relationship between stress levels and academic performance among college students. In one large course, you collect data on students' self-reported stress levels and their scores on the most recent quiz. Earlier in the course, we learned that the relationship between these two variables could be visually displayed on a scatterplot. This week, we’ll learn how to summarize the relationship between these two variables numerically using a correlation coefficient. If you find a significant negative correlation, it suggests that higher stress levels are associated with lower scores, providing valuable insights into how stress and academic performance are related.
After learning about correlation, you’ll delve into regression. Regression allows us to predict a dependent variable based on one or more independent variables, making it a frequently used tool in psychological research. Linear regression takes correlation a step further by not only determining if there is a relationship but also modeling it to make predictions. In essence, while correlation tells us about the strength and direction of the relationship between two variables, regression enables us to make predictions.
Overview
For this assessment, 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. Additionally, you will demonstrate your understanding of simple linear regression.
Preparation
Before you begin this assessment, complete the following:
- Download the Assessment 4 Worksheet [DOCX]. I will attach
- Download the Assessment_4a_Data.csv file. i will attach
- Download the Assessment_4b_Data.csv file. i will attach
Competencies Measured
By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and scoring guide criteria:
- Competency 1: Communicate research findings using data visualizations.
- Check the assumptions of Pearson’s r using graphs.
- 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 hypothesis test.
- Interpret the statistical significance of a simple linear regression model.
- Competency 3: Conduct statistical analyses to address research questions in the social sciences.
- Use JASP to construct a scatterplot and histograms.
- Use JASP to compute a correlation coefficient.
- 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 6: Explain why measurement of research variables must be reliable and valid.
- Discuss how your correlation can be used to support a specific type of reliability or validity.
- 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.