2. Quantitative Data Analysis and Interpretation
Chapter 13
Quantitative Data Analysis and Interpretation

3. Chapter Objectives edit questionnaire and interview responses
handle blank responses
set up the coding key for the data set and code the data
categorise data and create a data file
use SPSS, Excel or other software programs for data entry and data analysis
get a ‘feel’ for the data
test the goodness of data
statistically test each hypothesis
interpret the computer results and prepare recommendations based on the quantitative data analysis

4. The Quantitative Data Analysis Process

5. Getting Data Ready for Analysis
Editing data
Handling blank responses
Coding
Categorising
Entering data

6. Editing Data
open-ended questions of interviews & questionnaires, or unstructured observations
editing should be done on same day data collected so respondents (if not anonymous) may be contacted for further info or clarification
incoming mailed questionnaire data
inconsistencies that can be logically corrected should be rectified and edited at this stage

7. Handling Blank Responses
throw out questionnaire if >25% of questions unanswered
handle a blank response to an interval-scaled item with a midpoint:
assign the midpoint in the scale
allow the computer to ignore the blank responses
assign the mean value of the responses
give mean of responses of this particular respondent to all other questions measuring this variable
give a random number within range for scale
linear interpolation from adjacent points

8. Coding using scanner sheets for collecting questionnaire data
use a coding sheet first to transcribe data from the questionnaire and then key in data

9. Coding of Fox Publishing Co. questionnaire

10. Categorising Group items measuring same concept together
Reverse numbering of negatively worded questions

11. Entering data
Enter data from scanner answer sheets directly into computer
Enter raw data through any software programme, eg SPSS Data Editor, Excel

12. Data Analysis Data analysis packages - SPSS for Windows, Excel
Objectives:
getting a feel for the data
testing the goodness of data
testing the hypotheses

13. Getting a Feel for the Data
Get mean, variance and standard deviation for each vaiable
See if all items, responses range over the scale, and not restricted to one end of the scale alone
Obtain Pearson Correlations for all variables
Tabulate your data
Descriptive statistics for your sample’s key characteristics (eg demographic details)
See Histograms, Frequency Polygons, etc

14. Testing Goodness of Data
For each variable measured, obtain:
Reliability
Split half
Internal consistency
Validity
Convergent
Discriminant
Factorial

15. Testing Hypotheses
Using appropriate statistical analysis, test hypotheses, eg:
t-test to test the significance of differences of the means of two groups
Analysis of variance (ANOVA) to test significance of differences among the means of more than two different groups, using the F test
Using regression analysis to establish the variance explained in the DV through independent variables

16. Cases Research Done in Wollongong Enterprises
Using SPSS
Analysis of Accounting Chair Data Set
Using Excel

17. Possible Biases that Could Creep into Research
Asking the inappropriate or wrong research questions
Insufficient literature survey and hence inadequate theoretical models
Measurement problems
Samples not being representative

18. Possible Biases (cont’d)
Problems with data collection
Researcher biases
Respondent biases
Instrument biases
Data analysis biases
Coding errors
Data punching & input errors
Inappropriate statistical analysis
Biases (subjectivity) in intepretation of results