0.1 Introduction

SPSS download student version: link
SPSS canvas data file: link
SPSS tutorial in english: link
SPSS tutorial in swedish: link
Observe that instructions become less and less detailed as you work your way through the tasks.
Open SPSS. Log into the course website at Canvas. Download the file Communicative Organizations Database.
SUPER IMPORTANT!! Save your data file and all changes so you can use it for the following workshops! Also save your output files or copy paste relevant parts to another document if you want to remember what you did in the previous workshop!

1 Workshop: Descriptive statistics

https://nils-holmberg.github.io/scom-r/edu/scom-skoc62/workshop-1.html

2 Workshop: Data transformation

https://nils-holmberg.github.io/scom-r/edu/scom-skoc62/workshop-2.html

3 Workshop: Bivariate analysis

3.1 Univariate analysis

Create a new summative index_infoaccess using the variables 6-11: info_work, info_org, info_vision, info_change, info_intracomm and info_mgmttrust. Note central tendency, dispersion and normal distribution.

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   6.00   18.00   23.00   21.49   26.00   30.00    1767

Take a look at the following variables: Age in years (see previous workshop), Gender, Role, and Org_type. Note the dispersion, central tendency and normal distribution of the variables as applicable. Are the variables quantitative (ratio/interval) or qualitative (nominal/ordinal)?
How can we think about the possible effects that the variables in 3.1b can have on the index in 3.1a? What does the index measure?

3.2 Bivariate analysis

Perform bivariate analyses for each of the variables in 3.1b and the index_infoaccess. Use compare means to compare with qualitative variables; use correlate to compare with quantitative variables. (NB: For tests of statistical significance when comparing means, please follow instructions given during the workshop (i.e. independent samples t-test and one-way ANOVA).)
Now perform bivariate analyses for each of the variables in 3.1b pairwise (i.e. compare them with each other, using cross tables, compare means, or correlation as applicable). Note any significant relationships.

3.3 Creating dummy variables

Recode Gender to a variable with only two categories, Female=1 and Male=0. Compare it with the original variable to make sure you coded correctly. Name it dummy_gender.


Female   Male  Other   <NA> 
  4262   2179     19   1631

 Factor w/ 3 levels "Female","Male",..: 1 2 1 1 1 1 2 2 2 1 ...

[1] "Female" "Male"   "Other"


  _male _female    <NA> 
   2179    4262    1650

Recode Role into three separate dummy variables as follows: Create three new variables named dummy_manager, dummy_communicator, and dummy_coworker. dummy_manager should have the value 1 for manager and 0 for all other values in the old variable. dummy_communicator should have the vale 1 for communicator and 0 for all other values in the old variable. dummy_coworker should have the value 1 for coworkers and 0 for all other values in the old variable. Compare the dummy variables with the original variable Role to make sure you coded correctly. Note that you will use these variables during the next workshop!


                    Manager                    Coworker 
                       1520                        6068 
Communicator or equivalent                         <NA> 
                        499                           4

[1] "Manager"                     "Coworker"                   
[3] "Communicator or equivalent "

THAT’S IT FOR TODAY! REMEMBER TO SAVE YOUR DATA AND OUTPUT FILES (THOSE ARE TWO DIFFERENT OPERATIONS)!

4 Workshop: Multivariate analysis

https://nils-holmberg.github.io/scom-r/edu/scom-skoc62/workshop-4.html

scom-r website