This course reviews and explains the basic statistical concepts and techniques that are used in the area of Management and Business Administration and MSc theses, and emphasizes the practical application of the various techniques using SPSS software.
Two main windows:
- Data editor = enter data and carry out statistical functions
o Data view = show, enter data
o Variable view = defining characteristics of variables within data editor
- Viewer = results
Other windows:
- Syntax editor = enter SPSS commands manually
Top bar:
- File = saving, opening, printing, export etc.
- Edit = cut, paste, options etc.
- View = gridlines, value labels
- Data = make changes to data editor
o Insert variable (add/subtract columns)
o Insert cases (add/subtract row)
o Split file (split up a grouped variable)
o Select cases (run analysis on a screen of cases)
- Transform = manipulate variables in some way; “recode” to change values of certain
variables; “compute” for transforming data
- Analyze = all statistical procedures; “descriptive statistics” for mean, mode, median
etc., general data exploration
- Correlate = correlation techniques
- Regression = linear, multiple linear, logistic
- Dimension reduction = factor analysis
- Scale = reliability analysis
- Graphs = histograms, scatterplots, box-whiskers etc.
- Utilities = comment on dataset
Start with empty dataset: “untitled”
- Row = entity (element) data
- Column = variable (no distinction between independent and dependent variables)
Variable can be “between-group” = subjects belong to either one or another test group, no
overlap
Use numbers to represent categories (Likert: 1 = bad, 2 = below average, 3 = average etc.)
Create variables:
1) Use “variable view” at bottom (Tab)
2) Rows in “variable view” = variable
Name = variable name (short, no spaces)
Type = numeric (numbers), string (names, lettercodes etc.), currency, date (longitudinal)
Label = longer, more intricate description of variable
Values = assign numbers to represent groups (1 = cat, 2 = dog, 3 = gerbil etc.)
Missing = assign numbers to missing data
Measure = nominal, ordinal, scale
Role = is a variable a “predictor” (input), an “outcome” (target) or both
“Split” = divides variable into sub-groups (age: 0-5, 6-10, 11-15 etc.)
“Partition” = selects part of the data
“None” = a variable without a pre-defined role
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