Remove or Replace: Header Is Not Doc Title Data Set Instru ✓ Solved

The grades.sav file is a sample SPSS data set. The fictional 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 about 35 students (N = 105). Last week, you converted grades2.dat to grades.sav. There are 21 variables in grades.sav.

Open your grades.sav file and go to the Variable View tab. Make sure you have the following values and scales of measurement assigned:

  • id: Student identification number, Nominal

  • lastname: Student last name, Nominal

  • firstname: Student first name, Nominal

  • gender: Student gender (1 = female; 2 = male), Nominal

  • ethnicity: Student ethnicity (1 = Native; 2 = Asian; 3 = Black; 4 = White; 5 = Hispanic), Nominal

  • year: Class rank (1 = freshman; 2 = sophomore; 3 = junior; 4 = senior), Scale

  • lowup: Lower or upper division (1 = lower; 2 = upper), Ordinal

  • section: Class section, Nominal

  • gpa: Previous grade point average, Scale

  • extcr: Did extra credit project? (1 = no; 2 = yes), Nominal

  • review: Attended review sessions? (1 = no; 2 = yes), Nominal

  • quiz1: Quiz 1: number of correct answers, Scale

  • quiz2: Quiz 2: number of correct answers, Scale

  • quiz3: Quiz 3: number of correct answers, Scale

  • quiz4: Quiz 4: number of correct answers, Scale

  • quiz5: Quiz 5: number of correct answers, Scale

  • final: Final exam: number of correct answers, Scale

  • total: Total number of points earned, Scale

  • percent: Final percent, Scale

  • grade: Final grade, Nominal

  • passfail: Passed or failed the course?, Nominal

Data Analysis & Application Template Learner Name Capella University Data Analysis and Application (DAA) Template. Use this file for all assignments that require the DAA Template. Although the statistical tests will change from week to week, 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 & 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.

Application:

  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.

References: Provide references in proper APA Style if necessary.

Paper For Above Instructions

Data Analysis Plan

The variables identified in the grades.sav dataset include demographic information and academic performance metrics for students. The scales of measurement for these variables are categorized into nominal, ordinal, and scale data types, as follows: The ID, last name, first name, gender, ethnicity, section, and pass/fail status are nominal variables; year and lower/upper division are ordinal variables; and GPA, quiz scores, final exam scores, total points, and final percentage are scale variables. These variables are vital for analyzing student performance and demographics in a structured manner.

The research question guiding this analysis is: "What factors significantly influence student performance as indicated by quiz and final exam scores?" The null hypothesis (H0) states that there are no significant relationships between student demographics and performance on assessments, while the alternate hypothesis (H1) posits that significant relationships do exist.

Testing Assumptions

The first assumption to test is the normality of the distribution of quiz and final exam scores. The Kolmogorov-Smirnov test and Shapiro-Wilk test outputs from SPSS will be pasted here to evaluate whether the assumptions of normality are met. If the p-value is greater than the significance level (commonly set at .05), we can conclude that the assumption of normality is satisfied.

Results & Interpretation

The SPSS outputs for the main inferential statistics used, likely ANOVA or regression analysis, will be detailed below. The results will include p-values, mean comparisons, and effect sizes to gauge the significance and impact of student demographics on performance metrics. Statistical interpretation will focus on p-values in relation to our alpha level, detailing whether to reject or fail to reject the null hypothesis based on computed statistics.

Statistical Conclusions

This analysis will summarize findings related to how demographics and prior performance impact student assessment outcomes. Limitations of the study may include sample size restrictions and the scope of variables included in the analysis. Potential biases due to uncontrolled variables also need consideration. Alternate explanations for observed results, such as external factors influencing student performance, will be discussed, paving the way for future research directions aimed at deeper explorations of student success factors.

Application

In fields such as education and social sciences, this type of analysis serves as a powerful tool to uncover relationships between various demographic factors and student performance. Using data from the SPSS analysis, educators can better understand performance disparities and create interventions tailored to improve outcomes across diverse student populations. The implications of such analyses could lead to informed policy decisions and resource allocations, fostering an equitable educational landscape.

References

  • Field, A. P. (2017). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.

  • Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for The Behavioral Sciences. Cengage Learning.

  • Field, A. (2013). Discovering Statistics Using R. Sage Publications.

  • Moore, D. S., Notz, W. I., & Fligner, M. A. (2013). The Basic Practice of Statistics. W.H. Freeman.

  • Trochim, W. M. (2006). Research Methods: Knowledge Base. Atomic Dog Publishing.

  • Yates, F. (1999). Statistics for the Social Sciences. Houghton Mifflin Company.

  • IBM Corp. (2021). IBM SPSS Statistics for Windows. IBM Corp.

  • Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

  • Kucker, S. C., & Harlow, L. L. (2020). Statistical Power Analysis. Routledge.

  • American Psychological Association. (2020). Publication Manual of the American Psychological Association. APA.