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Chapter 13 Quantitative Data Analysis and Interpretation

Chapter 13 Quantitative Data Analysis and Interpretation. 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

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Chapter 13 Quantitative Data Analysis and Interpretation

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  1. Chapter 13 Quantitative Data Analysis and Interpretation

  2. 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

  3. The Quantitative Data Analysis Process

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

  5. 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

  6. 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

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

  8. Coding of Fox Publishing Co. questionnaire

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

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

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

  12. 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

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

  14. 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

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

  16. 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

  17. 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

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