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 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

The Quantitative Data Analysis Process

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

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

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

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

Coding of Fox Publishing Co. questionnaire

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

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

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

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

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

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

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

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

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