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Data Analysis and Application (DAA) TemplateUse this file for all assignments that require the DAA Template. Although the statistical tests will change from assessment to assessment, the basic...

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Data Analysis and Application (DAA) Template

Use this file for all assignments that require the DAA Template. Although the statistical tests will change from assessment to assessment, the basic organization and structure of the DAA remains the same.

Update the title of the template. All should be written in narrative form (delete numbered lists).

Data Analysis Plan

  1. Name the variables and the scales of measurement.

  2. State your research question, null and alternate hypothesis.

Testing Assumptions

  1. Paste the SPSS output for the given assumption.

  2. Summarize whether or not the assumption is met.

Results and Interpretation

  1. Paste the SPSS output for main inferential statistic(s) as discussed in the instructions.

  2. Interpret statistical results as discussed in the instructions.

Statistical Conclusions

  1. Provide a brief summary of your analysis and the conclusions drawn.

  2. Analyze the limitations of the statistical test.

  3. Provide any possible alternate explanations for the findings and potential areas for future exploration.


  1. Think of your own field of study. Describe how this type of analysis might be used in your field.

  2. Analyze what the value and potential implication of such an analysis would be.


Provide references in proper APA Style.

Answered 1 days After Sep 08, 2023


Pratibha answered on Sep 10 2023
27 Votes
Statistical Analysis
Name of the student
Student ID
Variable Names and Scales of Measurement
Variable names and scales of measurement for the provided dataset:
    Data type
    Unique identifie
    Last name of student
    First name of student
    Gender Identity (1 = Male, 2 = Female)
    Ethnicity (1 = Asian, 2 = Black, 3 = Hispanic, 4 = White)
    Student Yea
    External credits
    Review session attended or not
    Quiz Scores
    Final exam score
    Total score
    Percentage score
    Grade (A,B, C,D)
    Pass/Fail (1=Pass, 0=Fail)
Research Question and Hypotheses
Research Question: "What factors are associated with student performance and whether they pass or fail?"
· Null Hypothesis ($H_0$): "There is no significant relationship between the independent variables (e.g., gender, ethnicity, GPA) and student performance (pass/fail)."
· Alternate Hypothesis ($H_A$): "There is a significant relationship between the independent variables and student performance."
For testing the relationship between independent variables (e.g., gender, ethnicity, GPA) and student performance (pass/fail), we can use a chi-squared test of independence. This test is appropriate when you have categorical independent variables and a categorical dependent variable like "pass/fail." Specifically, we can perform a chi-squared test for independence or a chi-squared test for association.
Testing Assumptions:
The chi-square test of independence has some assumptions that need to be considered when applying it to a dataset. These assumptions are related to the nature of the data and the appropriateness of the test. Here are the key assumptions for the chi-square test of independence:
1. Independence of Observations: The observations in the contingency table (cross-tabulation) should be independent of each other. In other words, the data points should not be influenced by or dependent on each other. Violations of this assumption can lead to inaccurate results.
2. Random Sampling: The data should come from a random sample or a well-defined sampling process. This ensures that the sample is representative of the population from which it was drawn.
3. Expected Cell Frequencies: The expected cell frequencies (the values that would be expected under the null hypothesis of independence) should be greater than or equal to 5 for most cells in the contingency table. This assumption is known as the "5 or more" rule.
4. If the expected cell frequencies are very small (less than 5), the chi-square test may not be appropriate, and alternative tests (e.g., Fisher's Exact Test) should be considered.
5. Categories are Mutually Exclusive: The categories or levels of the categorical variables should be mutually exclusive, meaning that each observation should belong to only one category.
6. Ordinal or Nominal Data: The chi-square test is most appropriate for categorical data that is either ordinal (categories have a natural order) or nominal (categories have no natural order).
7. Large Sample Size: While the chi-square test is robust to violations of normality assumptions, it is more reliable with larger sample sizes. Small sample sizes can lead to less reliable results.
Results and Interpretation

The descriptive statistics presented summarize various characteristics of a dataset containing 105 valid data points across multiple variables. Here's a detailed summary of the key findings:
· The dataset appears to include a mix of categorical and numerical variables. Among the numerical variables, the mean GPA stands at 2.862, indicating an average academic performance. The "total" variable, with a mean of 61.838, suggests a scoring system or total points, and the "percent" variable has a mean of 100.086, possibly representing percentages.
· The categorical variables include "genderidentity" and "ethnicity," with means of 1.714 and 3.352, respectively. These values likely co
espond to categories or codes representing gender identity and ethnicity, but the specific interpretations would require additional context.
· The dataset includes several "quiz" variables (quiz1 to quiz5), each with mean values around 7.5, indicating relatively consistent quiz scores. The "passfail" variable has a mean of 0.543, suggesting that the majority of observations fall within one category.
· Standard e
ors, confidence intervals, and standard deviations provide information about the precision of the mean estimates and the variability in the data.
· The Shapiro-Wilk tests indicate that some variables deviate from a normal distribution, as reflected in their small p-values.
· In conclusion, these descriptive statistics offer insights into the central tendency, variability, and...

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