data edit code

8/11/2019 Data Edit Code

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Learning Objectives

1. Explain the concepts of editing and coding2. List the important considerations in editing and

coding3. List and explain the key issues in error-checking

and data transformation4. Explain the contents and uses of a code book5. Edit and code completed questionnaires


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PROCESSING OF DATA

The collected data in research is processed and analyzed tocome to some conclusions or to verify the hypothesismade.

Processing of data is important as it makes further analysis ofdata easier and efficient. Processing of data technicallymeans

1. Editing of the data

2. Coding of data

3. Classification of data

4. Tabulation of data.


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Overview of the Stages of Data Analysis


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EDITING

The process of checking and adjustingresponses in the completed questionnairesfor omissions, legibility, and consistency andreadying them for coding and storage


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Types of Editing

1. Field EditingPreliminary editing by a field supervisor on thesame day as the interview to catch technicalomissions, check legibility of handwriting, andclarify responses that are logically orconceptually inconsistent.

2. In-house Editing or central

Editing performed by a central office staff; often dome morerigorously than field editing

1. Backtracking

2. Allocating missing values

3. Plug values


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Purpose of Editing

1. For consistency between and amongresponses

2. For completeness in responses to reduceeffects of item non-response

3. To better utilize questions answered out oforder

4. To facilitate the coding process


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Editing for CompletenessItem Nonresponse

The technical term for an unanswered question on anotherwise complete questionnaire resulting in missingdata.

Plug Value An answer that an editor plugs in to replace blanks ormissing values so as to permit data analysis; choice ofvalue is based on a predetermined decision rule.

ImputeTo fill in a missing data point through the use of astatistical algorithm that provides a best guess for themissing response based on available information.


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Facilitating the CodingProcess

Data Clean-upChecking written responses for any stray marks

Editing And Tabulating Dont Know Answers Legitimate dont know (no opinion) Reluctant dont know (refusal to answer) Confused dont know (does not understand)


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Editing (contd) Pitfalls of Editing

Allowing subjectivity to enter into the editing process.Data editors should be intelligent, experienced, andobjective.

Failing to have a systematic procedure for assessing thequestionnaires developed by the research analyst An editor should have clearly defined decision rules tofollow.

Pretesting EditEditing during the pretest stage can prove very valuable forimproving questionnaire format, identifying poor instructionsor inappropriate question wording.


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CODING

The process of identifying and classifying eachanswer with a numerical score or othercharacter symbol given by respondent

The numerical score or symbol is called a code ,and serves as a rule for interpreting, classifying,and recording data

Identifying responses with codes is necessary if

data is to be processed by computer


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Coding - ContinuedCoded data is often stored electronically in the form of adata matrix - a rectangular arrangement of the data intorows (representing cases) and columns (representingvariables)

The data matrix is organized into fields, records, and files:Field: A collection of characters that represents a singletype of dataRecord: A collection of related fields, i.e., fields related tothe same case (or respondent)File: A collection of related records, i.e. records related tothe same sample


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CodingCodebook formulation it is the formalstandardization for all the variables under study.While designing we must take care of :

Appropriateness to the research objectiveComprehensive optionsMutually exclusive optionsSingle variable entry


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Coding

Coding of closed ended structured questionsDichotomous questionsRanking questionsChecklist /multiple responsesScaled questionsMissing values


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Key Issues in Coding1. Pre cod ing and p os t -cod ing2. Pre-Cod ing Fixed-Altern ative Questio ns (FA Qs)

-Writing codes for FAQs on the questionnairebefore the data collection

3. Coding Open-Ended Ques t ions - A 3-stageprocess:

(a) Perform a test tabulation, (b) Devise a codingscheme, (c) Code all responsesTwo Rules For Code Construction are:

a) Coding categories should be exhaustive

b) Coding categories should be mutually exclusive andindependent


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Issues in Coding - Continued

3. Maintain ing a Cod e B oo k - A book thatidentifies each variable in a study, the variablesdescription, code name, and position in the datamatrix

4. Produ c t ion Cod ing - The physical activity oftransferring the data from the questionnaire or datacollection form [to the computer] after the data hasbeen collected. Sometimes done through a codingsheet ruled paper drawn to mimic the data matrix

5. Com bin ing Ed i ting and Cod ing


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AFTER CODING ..

1. Data Ent ry - The transfer of codes fromquestionnaires (or coding sheets) to a computer.Often accomplished in one of three ways:

a) On-line direct data entry

b) Optical scanning for highly structuredquestionnaires

c) Keyboarding data entry via a computerkeyboard; often requires verification


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After Coding - Continued

2. Error Ch eck in g Verifying the accuracy ofdata entry and checking for some kinds ofobvious errors made during the data entry.

Often accomplished through frequencyanalysis.


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After Coding - Continued

3. Data Trans form at ion Converting some of thedata from the format in which they were entered toa format most suitable for particular statisticalanalysis.Often accomplished through re-coding, to:

reverse-score negative (or positive) statementsinto positive (or negative) statements;

collapse the number of categories of a variable


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EDITING REQUIRES SOME CAREFULCONSIDERATIONS:

Editor must be familiar with the interviewers mind set, objectivesand everything related to the study.Different colors should be used when editors make entry in thedata collected.They should initial all answers or changes they make to the data.The editors name and date of editing should be placed on thedata sheet.


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CODING:

Classification of responses may be done on the basis ofone or more common concepts.

In coding a particular numeral or symbol is assigned to theanswers in order to put the responses in some definitecategories or classes.

The classes of responses determined by the researchershould be appropriate and suitable to the study.

Coding enables efficient and effective analysis as theresponses are categorized into meaningful classes.

Coding decisions are considered while developing ordesigning the questionnaire or any other data collection tool.

Coding can be done manually or through computer.


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CLASSIFICATION:

Classification of the data implies that the collected raw

data is categorized into common group having commonfeature.

Data having common characteristics are placed in acommon group.

The entire data collected is categorized into various groupsor classes, which convey a meaning to the researcher.

Classification is done in two ways:

1. Classification according to attributes.

2. Classification according to the class intervals.


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CLASSIFICATION ACCORDING THE THE ATTRIBUTES:

Here the data is classified on the basis of common

characteristics that can be descriptive like literacy, sex,honesty, marital status e.t.c. or numeral like weight, height,income e.t.c.

Descriptive features are qualitative in nature and cannot be

measured quantitatively but are kindly considered whilemaking an analysis.

Analysis used for such classified data is known as statisticsof attributes and the classification is known as theclassification according to the attributes.


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TABULATION:

The mass of data collected has to be arranged in some kind ofconcise and logical order.

Tabulation summarizes the raw data and displays data in formof some statistical tables .

Tabulation is an orderly arrangement of data in rows and

columns.OBJECTIVE OF TABULATION :

1. Conserves space & minimizes explanation and descriptivestatements.

2. Facilitates process of comparison and summarization.

3. Facilitates detection of errors and omissions.

4. Establish the basis of various statistical computations.


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BASIC PRINCIPLES OF TABULATION :

1. Tables should be clear, concise & adequately titled.

2. Every table should be distinctly numbered for easyreference.

3. Column headings & row headings of the table should beclear & brief.

4. Units of measurement should be specified at appropriateplaces.

5. Explanatory footnotes concerning the table should beplaced at appropriate places.

6. Source of information of data should be clearly indicated.


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7. The columns & rows should be clearly separated with

dark lines8. Demarcation should also be made between data of oneclass and that of another.

9. Comparable data should be put side by side.

10. The figures in percentage should be approximated beforetabulation.

11. The alignment of the figures, symbols etc. should beproperly aligned and adequately spaced to enhance thereadability of the same.

12. Abbreviations should be avoided.


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Post tabulation

Exploratory analysisStatistical software packages

MS ExcelMinitabSystem for statistical analysis (SAS)Statistical package for social science (SPSS)


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ANALYSIS OF DATA

The important statistical measures that are used to analyzethe research or the survey are:

1. Measures of central tendency(mean, median & mode)

2. Measures of dispersion(standard deviation, range, meandeviation)

3. Measures of asymmetry(skew ness)

4. Measures of relationship etc.( correlation and regression)

5. Association in case of attributes.

6. Time series Analysis


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TESTING THE HYPOTHESIS

Several factor are considered into the determination of the

appropriate statistical technique to use when conducting ahypothesis tests. The most important are as:

1. The type of data being measured.

2. The purpose or the objective of the statistical inference.

Hypothesis can be tested by various techniques. Thehypothesis testing techniques are divided into two broad

categories:1. Parametric Tests.

2. Non- Parametric Tests.


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PARAMETRIC TESTS:

These tests depends upon assumptions typically that thepopulation(s) from which data are randomly sampledhave a normal distribution. Types of parametric tests are:

1. t- test

2. z- test

3. F- test

4. 2- test


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NON PARAMETRIC TESTS

The various types of Non Parametric Tests are:

1. Wilcox on Signed Rank Test ( for comparing twopopulation)

2. Kolmogorov Smirnov Test ( to test whether or not thesample of data is consistent with a specified distribution

function)3. Runs Tests (in studies where measurements are made

according to some well defined ordering, either in time orspace, a frequent question is whether or not the average

value of the measurement is different points in thesequence. This test provides a means of testing this.

4. Sign Test (this is single sample test that can be usedinstead of the single sample t- test or paired t- test.

5. Chi square test


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INTERPRETATION:

Interpretation is the relationship amongst the collected data,with analysis. Interpretation looks beyond the data of theresearch and includes researches, theory and hypothesis.Interpretation in a way act as a tool to explain theobservations of the researcher during the research periodand it acts as a guide for future researches.

WHY Interpretation?

-the researcher understands the abstract principle underlyingthe findings.

-Interpretation links up the findings with those of other similarstudies.

-The researcher is able to make others understand the realimportance of his research findings.


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