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Data Analysis, t-Test Application and Interpretation
Megan Bella
Capella University, PSY-FPX7864, Assessment 3
, 2
Data Analysis and Application (DAA) Template
A t-Test can be used to compare data sets and access relationships among variables. In
this paper, we will review data from a class and seek to test hypotheses using SDSS data tools,
including t-Test.
Data Analysis Plan
The data set provided in the Grades.sav contains various performance and demographic
items for 105 students. The data can be used to examine correlations and test assumptions
about the student population. The two data sets that will be most used in this paper will be
gender and GPA. The research question we will address is as follows: Do female students have
higher GPA than male students? Our null analysis will tell us that females do not have higher
GPA than male students. Our alternative hypothesis will be that females have statistically
significantly higher GPA.
For the purpose of the data, female data is represented by a 1 and male data with a 2. Gender is
the predictor variable, where GPA is variable. The student GPA is considered continuous data.
That is, there are a range of possible scores in a numerical value between two values. The student
gender is considered to be categorical data. That is, the data represents a finite number of
categories or distinct groups. There are 105 students (N size) and the alpha used will be 0.05.
The use of t-Test will determine if there is a significant correlation in GPA and gender from a
statistical standpoint.
Testing Assumptions
The histogram below shows normal distribution. There are no outliers. We can see here
that the histogram is leptokurtic, meaning several student GPAs are greater than what would be
Data Analysis, t-Test Application and Interpretation
Megan Bella
Capella University, PSY-FPX7864, Assessment 3
, 2
Data Analysis and Application (DAA) Template
A t-Test can be used to compare data sets and access relationships among variables. In
this paper, we will review data from a class and seek to test hypotheses using SDSS data tools,
including t-Test.
Data Analysis Plan
The data set provided in the Grades.sav contains various performance and demographic
items for 105 students. The data can be used to examine correlations and test assumptions
about the student population. The two data sets that will be most used in this paper will be
gender and GPA. The research question we will address is as follows: Do female students have
higher GPA than male students? Our null analysis will tell us that females do not have higher
GPA than male students. Our alternative hypothesis will be that females have statistically
significantly higher GPA.
For the purpose of the data, female data is represented by a 1 and male data with a 2. Gender is
the predictor variable, where GPA is variable. The student GPA is considered continuous data.
That is, there are a range of possible scores in a numerical value between two values. The student
gender is considered to be categorical data. That is, the data represents a finite number of
categories or distinct groups. There are 105 students (N size) and the alpha used will be 0.05.
The use of t-Test will determine if there is a significant correlation in GPA and gender from a
statistical standpoint.
Testing Assumptions
The histogram below shows normal distribution. There are no outliers. We can see here
that the histogram is leptokurtic, meaning several student GPAs are greater than what would be
