PROCESSING DATA

Steps in data processing

Sources of Raw Data Interviews Questionnaires Observation Focus groups Experiments Secondary data (Sometimes not raw)

Preparing Data for Analysis If you intend to undertake quantitative analysis we recommend you consider: The type of data (level of numerical measurement) The format in which your data will be input to the analytical software The impact of data coding on subsequent analysis. The need to weight cases The method you intend to use to check data for errors

Questions of Importance The following questions should be asked: How do you find answers to your research questions? How do you prove or disprove your hypothesis if you had one? How do you make sense of the information collected? How should the information be analysed to achieve the objectives of your study?

Irrespective of the method of data collection, the information collected is called raw data or simply data The first step in processing your data is to ensure that data are clean ie free from inconsistencies and incompleteness. This process of cleaning is called editing.

Editing Data Editing consists of scrutinising the completed research instrument to identify and minimise, as far as possible, errors, incompleteness, misclassification and gaps in the information obtained from the respondents.

Sometimes even the best investigators can: Forget to ask a question Forget to record a response Wrongly classify a response Write only half a response Write illegibly

In the case of a questionnaire, similar problems can crop up. These problems to a great extent can be reduced simply by: Checking the contents for completeness Checking the responses for internal consistency

There are several ways of minimising such problems: 1. By inference- certain questions in a research instrument may be related to one another and it might be possible to find out the answers to one question from the answer to another. Be careful not to introduce new errors into data

2.By recall- if the data is collected by means of interviews, sometimes it might be possible for the interviewer to recall a respondent’s answers Again, you must be extremely careful

3. By going back to the respondent- if the data has been collected by means of interviews or the questionnaire contain some identifying information, it is possible to visit or phone a respondent to confirm or ascertain an answer. This is of course expensive and time consuming

Ways of Editing Data There are two ways of editing data: A. Examining answers to one question or variable at a time. B. Examine answers to all questions at the same time, that is examine the responses given by a respondent.

Coding Data Having cleaned the data, the next step is to code it. The method of coding is largely by two considerations: The way a variable has been measured in your research instrument The way you want to communicate the findings about a variable to your readers.

For coding the first level of distinction is whether a set of data is qualitative or quantitative in nature For qualitative data a further distinction is whether the information is descriptive in nature or is generated through discrete qualitative categories.

The way you proceed with the coding depends upon the measurement scale used in the measurement of a variable and whether a question is open ended or closed ended. The type statistical procedures that can be applied to a set of information depend upon the measurement scale which a variable was measured in the research instrument. Eg mean, mode etc

The process of converting information into numerical values is called coding. Coding of data involves four steps: 1. Developing a code book 2. Pre-testing the book 3. Coding the data 4. Verifying the code data

Developing a Code book A code book provides a set of rules for assessing numerical values to answers obtained from responses. To develop a code book to prepare data for computer analysis, it is important to know a little about the working computers and the programmes being used.

Examples of questions 1. Please indicate: A) your current age ---------------- B) Your marital status Currently married --------- Living in a de facto relationship ---- Separated ------------- Divorced ------------- Never married ---------------

2. Specify the level of education Area of study eg accounting Diploma Bachelors degree Masters degree PhD Secondary school a

CODE BOOK 1. a. Code Age 20 -24 1 25-29 2 30- 34 3 35- 39 4 40- 44 5 25-29 2 30- 34 3 35- 39 4 40- 44 5 45-45 6 No Response 9

1.b. Code Marital status Currently married 1 Living in a de facto relationship 2 Separated 3 Divorced 4 Never married 5 No Response 9

2. Education level Code Diploma 1 Bachelors Degree 2 Masters 3 PhD 4 Secondary school 5 No Response 9

Area of study. Code Accounting 1 Business studies 2 Commerce 3 Economics 4 History 5 No Response 15

Pre-testing the code book Once a code is designed, it is important to pre-test it if any problems before you code your data A pre-test involves selecting a few questionnaires/interview schedules and actually coding the responses to ascertain any problems in coding. It is possible that you may not have provided for some responses and therefore will be unable to code them Change your code book, if you need to, in the light of the pre-test

Coding the data Once your code book is finalised, the next step is to code the raw data. There are two ways of doing this: Coding on the questionnaire/interview schedule itself, if space for coding was provided at the time of constructing the research instrument Coding on separate code sheets that are available for

Developing a frame of analysis A frame of analysis should specify: Which variables you are planning to analyse How they should be analysed What cross tabulation you need to work out Which variable you need to combine to construct your major concepts or to develop indices Which variables are to be subjected to which statistical procedure

Frequency distribution Frequency distribution group respondents into the sub-categories into which variables have been divided. Frequency distribution is for the following variables. Age Marital status Education etc

Cross Tabulation Cross tabulation analyse two variables usually independent and dependent or attribute to determine if there is a relationship. The sub-categories of both the variables are cross-tabulated to ascertain if a relationship exist between them.

Analysing data Coded data can be analysed manually or with the help of a computer. However, manual analysis is only useful for calculating frequencies and for simple cross tabulations. In the current days data can be coded on computers statistical packages

Computer packages Excell SPSS Microfit Limdep GAMS RATS Eviews etc

The role of statistics Statistics have a role only when you have collected the required information adhering to the requirements of each operational step of the research process