2c Spss Setting Up A Data Set Exerciseinstructions Due Date 7721 ✓ Solved

2c: SPSS Setting up a Data Set Exercise Instructions & Due date: 7/7/21 variable is important to determine when you are deciding which statistical measure to use. Review the information below to decide the variable type (nominal, ordinal, interval, ratio) for each of the four data descriptions (School ID, School Region, Enrollment, Academic Rank) in the chart below. List each type of data and its corresponding variable type and briefly state why you chose that variable type for each. Now practice using SPSS to DUPLICATE the example on pages using the Chapter 2 Data Set 1. All data sets are also in the back of the textbook in Appendix C.

All SPSS outputs should be copied and pasted to a Word document for submission to Canvas. School ID School Region Enrollment Academic Rank A278 West 56 1 X044 East 825 2 P416 North 134 3 Y489 North 152 4 M223 West 79 5 D126 South 345 6 J013 East 924 7 H156 South 256 8 Rubric 50-Point SPSS Exercise Rubric Criteria Ratings Pt s This criterion is linked to a Learning Outcome Narrative Content 30 pts Full Marks Correct interpretation of SPSS output. Demonstrated foundational competency/ understanding of content/ exercise purpose and value. Written correctly in APA format. 27 pts Above Average Correct interpretati on of SPSS output.

Not written consistentl y per APA standards. 24 pts Average Correct interpretatio n of SPSS output. Narrative contains omissions or errors. Not written correctly per APA format. 15 pts Below Average Incorrect interpretati on of SPSS output.

Lack of understand ing of exercise fundament als. 30 pt s This criterion is linked to a Learning Outcome SPSS Content 20 pts Full Marks SPSS output correct and complet 18 pts Above Average SPSS output mostly correct and 16 pts Averag e SPSS partially correct or complet 10 pts Below Average SPSS output incorrect or incomple 20 pt s e and include d in the paper. complet e and included in the paper. e and include d in the paper. te or not included in the paper. Total Points: 50

Paper for above instructions

Introduction

Data analysis is an essential aspect of research that guides decision-making and provides insights. In order to perform effective data analysis, understanding the types of data variables and how to represent them in statistical software like SPSS (Statistical Package for the Social Sciences) is critical. This exercise focuses on classifying data types, setting up a data set in SPSS, and interpreting the output generated.

Classification of Variables

In this exercise, we have four variables: School ID, School Region, Enrollment, and Academic Rank. Below is the classification of each variable based on their types:

1. School ID

- Variable Type: Nominal
- Explanation: The School ID is a unique identifier assigned to each school. It serves to distinguish each school from another, hence it does not possess any inherent ranking or order. Nominal data is characterized by categories without a natural order (Meyers, Gamst, & Guarino, 2013).

2. School Region

- Variable Type: Nominal
- Explanation: The School Region represents different geographic areas where the schools are located (e.g., West, East, North, South). This variable also lacks a numerical order and consists of categorical values, qualifying it as nominal data (Field, 2013).

3. Enrollment

- Variable Type: Ratio
- Explanation: Enrollment signifies the number of students in each school, which can be measured and has a true zero point (i.e., no students). Ratio data has meaningful ratios and intervals, making it suitable for a variety of statistical analyses (Gravetter & Wallnau, 2017).

4. Academic Rank

- Variable Type: Ordinal
- Explanation: Academic Rank, ranging from 1 to 7, shows a ranking of schools based on some metric, such as academic performance. Even though '1' is better than '2,' the differences between ranks do not necessarily represent equal intervals, qualifying it as ordinal data (Tabachnick & Fidell, 2019).

Setting Up the Data Set in SPSS

After identifying the variable types, the next step is to input these variables into SPSS.

Steps to Input Data

1. Open SPSS: Launch the SPSS software on your computer.
2. Data View/Variable View: Switch to the 'Variable View' tab at the bottom.
3. Input Variable Information:
- Name: Assign the name (i.e., School_ID, School_Region, Enrollment, Academic_Rank).
- Type: Set the data type (e.g., String for nominal values; Numeric for ratio and ordinal values).
- Width/Decimals: Adjust width and decimal places as necessary.
- Values: Assign categorical values for the School Region (e.g., 1 = West, 2 = East, etc.).
- Ensure that missing values are set appropriately.
4. Data Entry: Switch to the 'Data View' tab and enter the actual data. Fill in the columns for each of the four variables based on the provided dataset.
5. Save the Dataset: Once all the data is entered, save the dataset for further analysis.

Copying SPSS Output

After completing the analysis using SPSS, it is crucial to document the output results. Here is how to properly copy and paste SPSS outputs into a Word document:
1. Run Analysis: Perform any statistical analyses if required (descriptive statistics or frequencies), and run the commands.
2. Output Window: Locate the output in the SPSS Output Viewer.
3. Copy Output: Highlight the desired output, right-click, and select ‘Copy’.
4. Paste into Word: Open your Word document and paste the output where suitable. Ensure proper formatting is maintained.

Interpretation of SPSS Output

The analysis will include descriptive statistics such as means and frequencies, which help in understanding the enrollment distribution across school regions and academic ranks.

Example Interpretation:

- Enrollment Data: By computing the mean enrollment across schools, we can discern which region has higher or lower student populations.
- Academic Rank Frequencies: A frequency table will provide insights into the performance distribution of schools. If a majority fall into rank '1' and '2', it establishes a greater concentration of high-performing schools in the dataset.

Conclusion

Understanding variable classification is paramount for ensuring appropriate statistical analyses in SPSS. Different variable types, such as nominal, ordinal, interval, and ratio, each serve distinct purposes and require specific analytical approaches. This exercise serves as a foundational framework for learners to practice data input and interpretation in SPSS.

References

1. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
2. Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for The Behavioral Sciences. Cengage Learning.
3. Meyers, L. S., Gamst, G., & Guarino, A. J. (2013). Applied Multivariate Research: Design and Interpretation. Sage Publications.
4. Pallant, J. (2020). SPSS Survival Manual. McGraw-Hill Education.
5. Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson Education.
6. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge.
7. Salkind, N. J. (2010). Statistics for People Who (Think They) Hate Statistics. Sage Publications.
8. Babbie, E. (2020). The Practice of Social Research. Cengage Learning.
9. Trochim, W. M. K. (2006). Research Methods Knowledge Base. Conjointly.
10. De Vaus, D. A. (2002). Surveys in Social Research. Routledge.
By following this structured approach, students not only learn how to work with SPSS but also appreciate the importance of variable type classification and its implications in data analysis.