Chapter 13Quantitative 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:1. Reliability

– Split half– Internal consistency

2. 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

1. Asking the inappropriate or wrong research questions

2. Insufficient literature survey and hence inadequate theoretical models

3. Measurement problems

4. Samples not being representative

Possible Biases (cont’d)

5. Problems with data collection– Researcher biases– Respondent biases– Instrument biases

6. Data analysis biases– Coding errors– Data punching & input errors– Inappropriate statistical analysis

7. Biases (subjectivity) in intepretation of results