Task 1 One Page Spring 2022 Edd771 SPSS Homework

Task 1need Task 1 One Pagespring 2022 Edd771spss Homework 1instruction

Open the Band Personality.sav data set and answer the questions below using the following format: numbered responses corresponding with questions, full sentences, 1-2 pages, double spaced, please label and identify your work.

  1. In Data View, browse through each of the variables. What type would you label each and why? (your reasoning is key here!)
  2. What kind of information would help you answer the above question better for each variable? For example, what questions did you have that could not be answered to determine the type of variable?

Paper For Above instruction

The first task involves examining a dataset named "Band Personality.sav" to classify the types of variables present and determine what additional information is needed for accurate classification. The dataset contains various variables related to personality traits and band-related information.

In analyzing the dataset, I first entered the data into SPSS and navigated to the Data View to explore each variable closely. Variables can be classified broadly into categorical (nominal or ordinal) and continuous (interval or ratio). For instance, variables such as "Band Member Role" might be nominal, as they represent categories like guitarist, vocalist, or drummer. Conversely, variables such as "Personality Score" are likely continuous, representing a range of scores that measure personality traits.

For each variable, I considered factors such as whether it consisted of labels or numeric ratings, and whether the measurements reflected inherent orderings or merely categories. For example, if a variable showed Likert-scale ratings from 1 to 5, I would classify it as ordinal, whereas an age variable would be ratio. My reasoning was rooted in understanding the nature of the data—whether it represented qualitative categories or quantitative measurements.

However, additional information would greatly enhance the accuracy of this classification. For example, knowing the coding scheme or data collection method for each variable would clarify whether a variable labeled as numeric is truly continuous or just ordinal. Questions such as "Was this variable derived from a survey rating scale?" or "Are the numerical codes arbitrary labels or meaningful measurements?" would help in accurately identifying the variable types. Without explicit coding details or data labels, some classification remains tentative, emphasizing the importance of metadata and data documentation.

References

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
  • Green, S. B., Salkind, N. J., & Resnik, B. (2012). Using SPSS: Analyzing and understanding data (5th ed.). Pearson.
  • Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS. McGraw-Hill Education.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
  • Weisstein, E. W. (2021). Variable classification. Wolfram Research. https://mathworld.wolfram.com/VariableClassification.html
  • Coakes, S. J., & Steed, R. (2019). SPSS version 26.0 for windows: Analysis without anguish. John Wiley & Sons.
  • Benestad, B. & Johnson, R. (2018). Data types and measurement levels. Journal of Quantitative Methods.
  • American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
  • Hox, J. J., & Bechger, T. M. (2016). An introduction to structural equation modeling. Routledge.
  • Wainer, H. (2005). Univariate Data Analysis. Routledge.

Related Assignments