Applied Data Analysis

Syllabus --- Fall 2017

 

 

Course Title:  Applied Data Analysis

 

Instructor:  Dr. Stephen J. Vodanovich

            

Required text:  Aldrich, J. O, & Cunningham, J. B. (2016). Using SPSS Statistics. SAGE Publications

 

Course Description:  The course will involve selecting appropriate statistical analyses, analyzing data using SPSS,

interpreting results, and communicating research findings for academic and applied audiences. 

 

 

Students should be able to:

 

1)  Understand he fundamentals of measurement and how this effects (e.g., limits) options for

      statistical analysis.

 

2)  Know the functions, and strengths and limitations, of various statistical analyses.

 

3)  Select appropriate statistical techniques for a specific set of research questions.

 

4)  Perform and interpret results from  a variety of statistical analyses (e.g., t-tests, ANOVA,

      reliability, regression, factor analysis)

 

5  Summarize and communicate research findings (verbally and in writing) for academic and

    applied audiences.

 

6)  Construct or evaluate the psychometric properties of measurement devices (e.g., tests, scales,

       surveys).

 

Approximate Timeline

Date

Topic

Chapters

Week 1

  Fundamentals of SPSS (e.g., data view, variable view,

  entering data, defining variables/values, levels of

  measurement)

 

3, 4

Week 2

  Descriptive statistics, checking data

 

  Data transformation (e.g., recode, compute)

 

5, 6, 11

Week 3

 Creating graphs

 

8, 9

Week 4

 Chi-square, Crosstabs; Correlation, Scatterplots

 

25, 19

Week 5

  t-tests, ANOVA, Krukal-Wallis Test

 

12, 13, 14, 15

Week 6

  2-way ANOVA, ANCOVA

 

16, 18

Week 7

  Multiple Regression

  • Basic concepts and assumptions
  • The regression equation (e.g., weights, part and partial corrections)
  • Types of analyses (e.g., stepwise, hierarchical)

 

21

Week 8

  Reliability (e.g., internal consistency) and 

  creating/evaluating tests (e.g., item difficulty, item x

  total score correlation)

NA

Week 9

  • Factor Analysis (Basic concepts and  assumptions)
  • Components (e.g., factor loadings, eigenvalues, scree plots) 
  • Types of approaches (e.g., exploratory, confirmatory)
  • Types of rotations (e.g., varimax, oblique)
  • Extractions (e.g., principle components, generalized least squares, principle axis factoring)

 

23

Week 10

  Structural Equation Modeling (SEM)

 

Grading: 

 

Assignments will be given that will involve choosing and performing the proper analysis in SPSS to answer specific research questions. 

Students will be required to summarize and interpret their findings for an academic and/or applied audience (typically a page or two in length). 

These summaries/interpretations will be in written form and via PowerPoint for selected students that will be scheduled in advance.

 

All assignments will be graded based upon the:

 

Choice of proper statistical analyses given the type of data and research question involved

Correct interpretation of results

Logical organization of material

Communication of statistical analyses and results in proper APA format (consistent with a Results section in an article)

Communication of any shortcomings and limitations with the analyses selected

 

 

Grading Scale

A

90 & above

B+

87-89

B

83-86

B-

80-82

C+

77-79

C

73-76

C-

70-72

D+

67-69

D

60-66

F

59 and below