Chapter 13 Quantitative Data Analysis and Interpretation

2. 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 dataand 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