Quantitative Data Analysis and Interpretation

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Chapter 13 Quantitative Data Analysis and
Interpretation
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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

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The Quantitative Data Analysis Process
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Getting Data Ready for Analysis

• Editing data
• Handling blank responses
• Coding
• Categorising
• Entering data

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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

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Handling Blank Responses

• throw out questionnaire if gt25 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

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Coding

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

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Coding of Fox Publishing Co. questionnaire
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Categorising

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

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Entering data

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

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Data Analysis

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

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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 samples key
  characteristics (eg demographic details)
• See Histograms, Frequency Polygons, etc

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Testing Goodness of Data

• For each variable measured, obtain
• Reliability
• Split half
• Internal consistency
• Validity
• Convergent
• Discriminant
• Factorial

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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

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Cases

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

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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

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Possible Biases (contd)

• 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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