Research Methodology

1
Research Methodology

• Lecture No 21
• Data Preparation and Data Entry

2
Recap Lecture

• In the last few lectures we discussed about
• Research Design
• The purpose, investigation type, researcher
  interference, study setting, unit of analysis,
  time horizon, Measurement of variables
• Sources of Data
• Sampling
• Experimental Design

3
Lecture Objectives

• Getting the data ready for analysis
• Data preparation
• Coding, codebook, pre-coding, coding rules
• Data entry
• Editing data
• Data transformation

4
Data Preparation and Description

• 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.

5
Getting the Data Ready for Analysis

• After data obtained through questionnaire, they
  need to be coded, keyed in, and edited.
• Outliers, inconsistencies and blank responses, if
  any, have to be handled in some way.

6
Coding

• Data coding involves assigning a number to the
  participants responses so, they can be entered
  into data base.
• 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.

7
Coding Cont.

• Numeric coding simplifies the researchers task
  in converting a nominal variable like gender to a
  1 or 2.

8
Code Construction

• There are two basic rules for code construction.
• First, the coding categories should be
  exhaustive, meaning that a coding category should
  exist for all possible responses.
• For example, household size might be coded 1, 2,
  3, 4, and 5 or more.
• The 5 or more category assures all subjects of
  a place in a category.

9
Code Construction Cont.

• Second, the coding categories should be mutually
  exclusive and independent.
• This means that there should be no overlap among
  the categories to ensure that a subject or
  response can be placed in only one category.

10
Code Construction Cont.

• Missing data should also be represented with a
  code.
• In the good old days of computer cards, a
  numeric value such as 9 or 99 was used to
  represent missing data.
• Today, most software will understand that either
  a period or a blank response represents missing
  data.

11
Codebook

• A codebook contains each variable in the study
  and specifies the application of coding rules to
  the variable.
• It is used by the researcher or research staff to
  promote more accurate and more efficient data
  entry.
• It is the definitive source for locating the
  positions of variables in the data file during
  analysis.

12
Sample Codebook
13
Pre-coding

• Pre-coding means assigning codebook codes to
  variables in a study and recording them on the
  questionnaire.
• Or you could design the questionnaire in such a
  way that apart from the respondents choice it
  also indicates the appropriate code next to it.
• With a pre-coded instrument, the codes for
  variable categories are accessible directly from
  the questionnaire.

14
Sample Pre-coded Instrument
15
Coding Open-Ended Questions

• One of the primary reasons for using open-ended
  questions is that insufficient information or
  lack of a hypothesis may prohibit preparing
  response categories in advance. Researchers are
  forced to categorize responses after the data are
  collected.

16
Coding Open-Ended Questions Cont.

• In the Figure on the next slide, question 6
  illustrates the use of an open-ended question.
  After preliminary evaluation, response categories
  were created for that item. They can be seen in
  the codebook.

17
Coding Open-Ended Questions Cont.
18
Coding Rules
19
Data Entry

• After responses have been coded, they can be
  entered into data base.
• Raw data can be entered through any software
  program.
• For example SPSS Data Editor.

20
Data Entry Cont.
21
Editing Data

• After data entered, the blank responses, if any,
  have to be handled in some way, and inconsistent
  data have to be checked and followed up.
• Data editing deals with detecting and correcting
  illogical, inconsistent, or illegal data and
  omissions in the information returned by the
  participants of study.

22
Editing Data Cont.
23
Field Editing

• Field Editing Review
• Entry Gaps ?? Callback
• Validates ?? Re-interviewing

24
Field Editing Review

• 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.

25

• If the forms are not completed soon, the field
  interviewer may not recall what the respondent
  said.
• Therefore, reporting forms should be reviewed
  regularly.

26
Field Editing Cont.

• Entry Gaps ?? Callback
• When entry gaps are present, a callback should be
  made rather than guessing what the respondent
  probably said.

27
Field Editing Cont.

• 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.

28
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.

29
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.

30
Central Editing Cont.

• 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.

31
Central Editing Cont.
Guidelines for Editors
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
32
Handling Dont Know Responses

• When the number of dont know (DK) responses is
  low, it is not a problem. However, if there are
  several given, it may mean that the question was
  poorly designed, too sensitive, or too
  challenging for the respondent.
• The best way to deal with undesired DK answers is
  to design better questions at the beginning.
• If DK response is legitimate, it should be kept
  as a separate reply category.

33
Data Transformation

• Data transformation, a variation of data coding,
  is a process of changing the original numerical
  representation of a quantitative value to another
  value.
• E.g The data given is in per year consumption
  and we need it for each month.
• Data are typically changed to avoid problems in
  the next stage of data analysis process.

34
Data Transformation Cont.

• For example, economists often use a logarithmic
  transformation so that the data are more evenly
  distributed.
• Data transformation is also necessary when
  several questions have been used to measure a
  single concept.
• E.g Intentions to leave is measured through 10
  questions which need to be transformed into a
  single value for a single respondent

35
Recap

• Questionnaire checking involves eliminating
  unacceptable questionnaires.
• These questionnaires may be incomplete,
  instructions not followed, missing pages, past
  cutoff date or respondent not qualified.
• Editing looks to correct illegible, incomplete,
  inconsistent and ambiguous answers.
• Coding typically assigns alpha or numeric codes
  to answers that do not already have them so that
  statistical techniques can be applied.

36
Recap Cont.

• Cleaning reviews data for consistencies.
  Inconsistencies may arise from faulty logic, out
  of range or extreme values.
• Statistical adjustments applies to data that
  requires weighting and scale transformations.