Instructions: Correlation Application and Interpretation
Complete an SPSS data analysis report to analyze correlation for assigned variables.Exploring the associations between some variables in the courseroom using correlations might provide some important information about learner success. You’ll need to pay attention to both magnitude, which is the strength of the association, and directionality, which is the direction (positive or negative) of the association. During this assessment, you’ll start learning about how to best approach correlational analyses like these and start getting some answers. You’ll explore the relationships that may or may not exist in your courseroom data.In this assessment, you’ll get a chance to run and interpret your first inferential statistics analysis: correlations. Your readings and the Course Study Guide will help you in your efforts.
INSTRUCTIONS
You will complete this assessment using the Data Analysis and Application Template [DOC] (also known as the DAA Template).
Refer to IBM SPSS Step-By-Step Guide: Correlations [PDF] for additional information on using SPSS for this assessment.
Review the Copy/Export Output Instructions [PDF] for help copying SPSS output into your DAA Template.
Use the Data Set Instructions [PDF] for information on the data set.
Refer to the Course Study Guide [PDF] for information on analyses and interpretation.
The
grades.sav
file is a sample SPSS data set. The data represent a teacher’s recording of student demographics and performance on quizzes and a final exam across three sections of the course. Each section consists of 35 students (N = 105). There are 21 variables in grades.sav.This assessment is on correlations. You will analyze the following variables in the grades.sav data set:
SPSS Variable Definition
Quiz 1 Quiz 1: number of correct answers
GPA Previous grade point average
Total Total number of points earned in class
Final Final exam: number of correct answers
The DAA Template has five sections:
The Data Analysis Plan.
Testing Assumptions.
Results and Interpretation.
Statistical Conclusions.
Application.
Step 1: The Data Analysis PlanIn Step 1:
Name the four variables used in this analysis and whether they are categorical or continuous.
Step 2: Testing AssumptionsTest for one of the assumptions of correlation—normality.
Paste the table in the DAA Template.
Step 3: Results and InterpretationIn Step 3:
Paste the SPSS output of the intercorrelation matrix for all specified variables:
First, report the lowest magnitude correlation in the intercorrelation matrix, including degrees of freedom, correlation coefficient, p value, and effect size. Interpret the effect size. Specify whether or not to reject the null hypothesis for this correlation.
Second, report the highest magnitude correlation in the intercorrelation matrix, including degrees of freedom, correlation coefficient, p value, and effect size. Interpret the effect size. Specify whether or not to reject the null hypothesis for this correlation.
Third, report the correlation between GPA and final, including degrees of freedom, correlation coefficient, p value, and effect size. Interpret the effect size. Analyze the correlation in terms of the null hypothesis.
Interpret statistical results against the null hypothesis, and state whether it is accepted or rejected.
Step 4: Statistical ConclusionsIn Step 4:
Provide a brief summary of your analysis and the conclusions drawn.
Analyze the limitations of the statistical test.
Provide any possible alternate explanations for the findings and potential areas for future exploration.
Step 5: ApplicationIn Step 5:
Analyze how you might use correlations in your field of study.
Name an independent variable and dependent variable that would work for such an analysis and why studying it may be important to the field or practice.
SOFTWARE
The following statistical analysis software is required to complete your assessments in this course:
IBM SPSS Statistics Standard or Premium GradPack, version 24 or higher, for PC or Mac.
You have access to the more robust IBM SPSS Statistics Premium GradPack.Please refer to the Statistical Software page on Campus for general information on SPSS software, including the most recent version made available to Capella learners.Make sure that your SPSS software is downloaded and installed with fully activated licensing on your computer and running properly within your operating system (PC or Mac). If you need help with these steps, refer to the SPSS Installation Helper.
1
Correlation Application and Interpretation
Name
Department; University
Course Code; Course Name
Tutor
Date
2
Correlation Application and Interpretation
The Data Analysis Plan
The four variables in the analysis include Quiz1, GPA, Total and Final all of which are
continuous. Critical from the assessment, the research question is; are the students’ final scores
associated with the GPA points? The question seeks to find the connection between overall
grades collected from the class and the final exam count, for the null hypothesis was whether
there is a connection between overall and last exam count. The question also seeks to establish
the connection between GPA and quiz one count
Hypothesis
Ho: Students’ scores in the final exam are not associated with the obtained GPA points.
Ha: Students’ scores in the final exam are associated with the obtained GPA points.
Table 1
Descriptive Statistics
Testing Assumptions
Descriptive Statistics
N
GPA
Minim Maxim
um
um
Mean
Statisti Statisti
Statisti
c
c
Statistic
c
105
1.08
4.00 2.8622
Std.
Deviation
Skewness
Statisti
Statistic
c
.71266
-.220
Kurtosis
Std. Statisti
Error
c
.236
-.688
Std.
Error
.467
quiz1
105
0
10
7.47
2.481
-.851
.236
.162
.467
final
105
40
75
61.84
7.635
-.341
.236
-.277
.467
3
total
105
Valid N
(listwise)
105
54
123 100.09
13.427
-.757
.236
1.146
.467
Table 1 above shows the skewness for variables quiz 1, GPA, total and final are shown as 0.851,
-0.220, -0.757, and -0.341. The kurtosis statistics, on the other hand, are shown as 0.162, -0.688.
1.146, and -0.277 for the variables quiz 1, GPA, total and final exam, respectively. From the
results, the skewness values depict a tailed distribution for the selected variables.
The table shows the results of the normality test on several variables from the grade’s dataset.
The study assumes the Shapiro-Wilk test for normality with a modest sample size of 105,
N=105. P=0.000, 0.004, 0.003, and 0.072 for variables quiz 1, GPA, total, and final, respectively,
for the Shapiro-Wilk test. The p-values in the table are less than 0.05, however with the
exception of the variable final, a p-value is larger than 0.05. The findings show that the variables
quiz 1, GPA, and total do not have a normal distribution, however the final characteristic does
(Sekaran & Bougie, 2019). As a result, the assumption of normality is not fulfilled for variables
quiz 1, GPA, and total; nevertheless, the variable Final almost verifies the normality test
assumption.
Table 2
Correlations
quiz1
Pearson Correlation
Sig. (2-tailed)
Correlations
quiz1
1
GPA
.152
.121
total
.797**
.000
final
.499**
.000
4
N
105
105
gpa
Pearson Correlation
.152
1
Sig. (2-tailed)
.121
N
105
105
**
total
Pearson Correlation
.797
.318**
Sig. (2-tailed)
.000
.001
N
105
105
**
final
Pearson Correlation
.499
.379**
Sig. (2-tailed)
.000
.000
N
105
105
**. Correlation is significant at the 0.01 level (2-tailed).
105
.318**
.001
105
1
105
.875**
.000
105
105
.379**
.000
105
.875**
.000
105
1
105
Results & Interpretation
The association between quiz1 and GPA score from each learner is indicated in Table 2
above as the variable with the least correlation. In this situation, the Pearson’s Correlation value
between the two variables is 0.152, with a p-value 0.121 larger than the statistical power a=0.05.
The data suggest that the null assertion saying that quiz 1 score is unrelated to learners’ GPA
score is correct and appropriate.
Furthermore, the data show that the relationship between the variables total and final
exam score has the largest correlational magnitude. The Pearson correlation value=0.875, with a
P=0.000 less than a=0.05, describes the association. As a result, the data reveal that the
connection reflects a significant positive interlink between the selected variables, rejecting the
null hypothesis. As a result of the data, it is correct to assume that a student’s total score is a
reflection of the final test score in that as the overall score for the quiz increases, so does the final
exam score, and vice versa.
Statistical Conclusions
5
Essentially, there exists no significant connection between GPA and quiz 1. It might be.
As a result, interrogation is determined on a small area of abstraction while GPA surrounds a
large span of the test, quizzes, and other tasks within an extended duration. Nevertheless, there
is a significant connection between the final exam and the overall count. It might be due to the
final exams mainly being at a maximum value than several other tasks or quizzes in the class.
Any restriction would be that may not assume a strong connection between either variable pair.
Applications
In the sector of applied behavior analysis, continuous data is utilized. Therefore, using
correlation efficiently brings behavior change through analysis of the connection between
behavior and environmental variables. For instance, there could be an evaluation of the
relationship between duration in school and demanding behaviors. If there is a positive
correlation between the two variables, it may change the period taken in school to lower
demanding behaviors in the coming times.
6
Reference
Sekaran, U., & Bougie, R. (2019). Research methods for business: A skill building approach.
John Wiley & Sons.
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