1. Data Preparation and Description Lecture 24 th

2. Recap If you intend to undertake quantitative analysis consider the following: type of data (scale of measurement); format in which your data will be input to the analysis software; SPSS, EVIEWS, STATA, NVIVO. impact of data coding on subsequent analyses (for different data types); methods you intend to use to check data for errors.

3. Recap Quantitative data can be divided into two distinct groups: categorical and numerical. Categorical data refer to data whose values cannot be measured numerically but can be either classified into sets (categories) according to the characteristics that identify or describe the variable or placed in rank order (Berman Brown and Saunders 2008).

4. Recap Numerical data, which are sometimes termed quantifiable’, are those whose values are measured or counted numerically as quantities (Berman Brown and Saunders 2008). This means that numerical data are more precise than categorical as you can assign each data value a position on a numerical scale. It also means that you can analyse these data using a far wider range of statistics. There are two possible ways of sub-dividing numerical data: into interval or ratio data and, alternatively, into continuous or discrete data.

5. Designing Diagrams and Tables For both diagrams and tables Does it have a brief but clear and descriptive title? Are the units of measurement used stated clearly? Are the sources of data used stated clearly? Are there notes to explain abbreviations and unusual terminology? Does it state the size of the sample on which the values in the table are based?

6. Data Preparation and Description

7. Data preparation includes editing, coding, and data entry. It is the activity that ensures the accuracy of the data and their conversion from raw form to reduced and classified forms that are more appropriate for analysis. Preparing a descriptive statistic summary is another preliminary step that allows data entry errors to be identified and corrected.

8. Data Editing The customary first step in analysis is to edit the raw data. Editing detects errors and omissions, corrects them when possible, and certifies the maximum data quality standards are achieved. The purpose is to guarantee that data are accurate, consistent with the intent of the question and other information in the survey, uniformly entered, complete, and arranged to simplify coding and tabulation.

9. Criteria Consistent Uniformly entered Uniformly entered Arranged for simplification Arranged for simplification Complete Accurate Data Editing

10. Field Editing Field Editing Review Entry Gaps  Callback Validates  Re-interviewing

11. Field Editing In large projects, field editing review is a responsibility of the field supervisor. It should be done soon after the data have been collected. During the stress of data collection, data collectors often use ad hoc abbreviations and special symbols. If the forms are not completed soon, the field interviewer may not recall what the respondent said. Therefore, reporting forms should be reviewed regularly.

12. Field Editing Entry Gaps  Callback When entry gaps are present, a callback should be made rather than guessing what the respondent probably said.

13. Field Editing Validates  Re-interviewing The field supervisor also validates field results by re- interviewing some percentage of the respondents on some questions to verify that they have participated. Ten percent is the typical amount used in data validation.

14. Central Editing Scale of Study  Number of Editors At this point, the data should get a thorough editing. For a small study, a single editor will produce maximum consistency. For large studies, editing tasks should be allocated by sections.

15. Central Editing Cont. Wrong Entry  Replacements Sometimes it is obvious that an entry is incorrect and the editor may be able to detect the proper answer by reviewing other information in the data set. This should only be done when the correct answer is obvious. If an answer given is inappropriate, the editor can replace it with a no answer or unknown.

16. Central Editing Fakery  Open-ended Questions The editor can also detect instances of armchair interviewing, fake interviews, during this phase. This is easiest to spot with open-ended questions.

17. Be familiar with instructions given to interviewers and coders Do not destroy the original entry Make all editing entries identifiable and in standardized form Initial all answers changed or supplied Place initials and date of editing on each instrument completed Central Editing Guidelines for Editors

18. Codebook Coding involves assigning numbers or other symbols to answers so that the responses can be grouped into a limited number of categories. In coding, categories are the partitions of a data set of a given variable. For instance, if the variable is gender, the categories are male and female. Categorization is the process of using rules to partition a body of data. Both closed and open questions must be coded.