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Periyar University, Salem-11
Periyar University, Salem-11

This document discusses the process of data analysis, which includes editing, coding, classification, tabulation, analysis, and interpretation of raw data. It defines data as raw facts or figures and information as processed data that allows conclusions to be drawn. The main steps outlined are questionnaire checking, editing, coding, tabulation, data cleaning, statistical adjustment, and selecting an analysis strategy. Editing involves reviewing questionnaires for accuracy. Coding transforms questionnaires into a format for analysis by assigning numbers to responses. Tabulation counts and organizes the coded data into statistical tables.

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Data Analysis
Topics to be covered Data Analysis : Editing, Coding, Classification, Tabulation, Analysis and Interpretation
Difference between Data and Information Any raw facts or figures is known as data. When the data is processed by doing statistical analysis and some conclusion can be drawn from it, it is known as information.
Steps in Processing of Data Questionnaire checking Editing Coding Tabulation Data Cleaning Statistically adjusting the data Selecting a Data Analysis Strategy
Questionnaire checking The initial step in questionnaire checking involves a check of all questionnaires for completeness and interviewing quality. A questionnaire returned from the field may be unacceptable for several reasons: 1. Part of the questionnaire may be incomplete. 2. The pattern of responses may indicate that the respondent did not understand or follow the instructions. 3. The responses show little variance. 4. The questionnaire is answered by someone who does not qualify for participation. 5. The returned questionnaire is physically incomplete, one or more pages are missing.
Editing Review of the questionnaires with the objective of increasing accuracy and precision. It consists of screening questionnaires to identify illegible, incomplete, inconsistent or ambiguous responses. This can be done in two stages: a) Field Editing Objective of field editing is to make sure that proper procedure is followed in selecting the respondent, interview them and record their responses. The main problems faced in field editing are: 1. Inappropriate Respondents Instead of house owners, tenant is interviewed. 2. Incomplete interviews, 3. Improper understanding, 4. Lack of consistency, 5. Legibility, 6, Fictitious interview Questionnaires are filled by interviewer himself without conducting the interview. b) Office Editing It is more thorough than field editing. Problems of consistency, rapport with respondents are some of the issues which get highlighted during office editing.
Example of Inconsistency: A respondent indicated that he doesnt drink coffee, but when questioned about his favorite brand, he replied BRU. Treatment of Unsatisfactory Responses Returning to the field Questionnaires with unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents. Assigning missing value Editor may assign missing values to unsatisfactory responses. This approach may be desirable if 1) the number of respondents with unsatisfactory responses is small, 2) the proportion of unsatisfactory responses for each of these respondents is small, or 3) the variables with unsatisfactory responses are not the key variables. Discarding unsatisfactory respondents This is possible only when proportion of unsatisfactory respondents is small or the sample size is large.
Coding Coding refers to those activities which helps in transforming edited questionnaires into a form that is ready for analysis. Coding speeds up the tabulation while editing eliminates errors. Coding involves assigning numbers or other symbols to answers so that the responses can be grouped into limited number of classes or categories. The code includes an indication of the column and data record it will occupy. For eg. Sex of respondents may be coded as 1for males and 2 for females. Questions Answers Codes 1. Do you own a vehicle? Yes 1 No 2 2. What is your occupation? Salaried S Business B Retired R
Tabulation Refers to counting the number of cases that fall into various categories. The results are summarized in the form of statistical tables. The raw data is divided into groups and sub-groups. The counting and placing of data in a particular group and sub-group are done. The tabulation involves: 1. Sorting and counting. 2. Summarising of data. Tabulation may be of two types: 1. Simple tabulation In simple tabulation, a single variable is counted. 2. Cross tabulation Includes two or more variables, which are treated simultaneously. Tabulation can be done entirely by hand, or by machine, or by both hand and machine.
Sorting and counting of data: Sorting can be done as follows: Format of a Blank table Table No. TITLE Number of children per family Head Note Unit of measurement Income (Rs) Tally Marks Frequencies 1000 IIII 4 1500 II 2 2000 III 3 Sub- Headin g Caption Body Foot note Total Sub heading indicates the row title or the row headings. Caption indicates what each column is meant for. Body of the table gives full information of
Kinds of Tabulation 1. Simple or one-way tabulation The multiple choice questions which allow only one answer may use on-way tabulation or univariate. The questions are predetermined and consist of counting the number of responses falling into a particular category and calculate the percentage. Example Table 14.1: Study of number of children in a family No. of children Family Percentage 0 10 5 1 30 15 2 70 35
2. Cross Tabulation or Two-way Tabulation This is known as Bivariate Tabulation.The data may include two or more variables. Eg. Popularity of a health drink among families having different incomes. Table 14.3: Use of Health Drink Income per month No. of children per family (0) 1 2 No. of families 1000 10 5 8 23 1001-2000 5 0 8 13 2001-3000 20 10 12 42
Data cleaning Includes consistency checks and treatment of missing responses. Although preliminary consistency checks have been made during editing, the checks at this stage are more thorough and extensive, because they are made by computer. Consistency checks Identify data that are out of range, logically inconsistent or have extreme values. For eg. A respondent may indicate that she charges long distance calls to a calling card, although she does not have one. http://www.facebook.com/mr.fortyseven
Treatment of missing responses Missing responses represent values of a variable that are unknown, either because respondents provided ambiguous answers or their answers were not properly recorded. 1. Substitute a Neutral Value A neutral value, typically the mean to the variable, is substituted for the missing responses. 2. Substitute an Imputed Response The respondents pattern of responses to other questions are used to impute or calculate a suitable response to the missing questions. 3. Casewise Deletion Cases or respondents with any missing responses are discarded from the analysis. 4. Pairwise deletion Instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation. As a result, different calculations in an analysis may be based on different sample sizes. http://www.facebook.com/mr.fortyseven
Statistically Adjusting the Data If any correction needs to be done for the statistical analysis, the data is adjusted accordingly. Selecting a Data Analysis Strategy The selection of a data analysis strategy should be based on the earlier steps of the marketing research process, known characteristics of the data, properties of statistical techniques and the background and philosophy of the researcher. http://www.facebook.com/mr.fortyseven

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8393438.ppt

  • 2. Topics to be covered Data Analysis : Editing, Coding, Classification, Tabulation, Analysis and Interpretation
  • 3. Difference between Data and Information Any raw facts or figures is known as data. When the data is processed by doing statistical analysis and some conclusion can be drawn from it, it is known as information.
  • 4. Steps in Processing of Data Questionnaire checking Editing Coding Tabulation Data Cleaning Statistically adjusting the data Selecting a Data Analysis Strategy
  • 5. Questionnaire checking The initial step in questionnaire checking involves a check of all questionnaires for completeness and interviewing quality. A questionnaire returned from the field may be unacceptable for several reasons: 1. Part of the questionnaire may be incomplete. 2. The pattern of responses may indicate that the respondent did not understand or follow the instructions. 3. The responses show little variance. 4. The questionnaire is answered by someone who does not qualify for participation. 5. The returned questionnaire is physically incomplete, one or more pages are missing.
  • 6. Editing Review of the questionnaires with the objective of increasing accuracy and precision. It consists of screening questionnaires to identify illegible, incomplete, inconsistent or ambiguous responses. This can be done in two stages: a) Field Editing Objective of field editing is to make sure that proper procedure is followed in selecting the respondent, interview them and record their responses. The main problems faced in field editing are: 1. Inappropriate Respondents Instead of house owners, tenant is interviewed. 2. Incomplete interviews, 3. Improper understanding, 4. Lack of consistency, 5. Legibility, 6, Fictitious interview Questionnaires are filled by interviewer himself without conducting the interview. b) Office Editing It is more thorough than field editing. Problems of consistency, rapport with respondents are some of the issues which get highlighted during office editing.
  • 7. Example of Inconsistency: A respondent indicated that he doesnt drink coffee, but when questioned about his favorite brand, he replied BRU. Treatment of Unsatisfactory Responses Returning to the field Questionnaires with unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents. Assigning missing value Editor may assign missing values to unsatisfactory responses. This approach may be desirable if 1) the number of respondents with unsatisfactory responses is small, 2) the proportion of unsatisfactory responses for each of these respondents is small, or 3) the variables with unsatisfactory responses are not the key variables. Discarding unsatisfactory respondents This is possible only when proportion of unsatisfactory respondents is small or the sample size is large.
  • 8. Coding Coding refers to those activities which helps in transforming edited questionnaires into a form that is ready for analysis. Coding speeds up the tabulation while editing eliminates errors. Coding involves assigning numbers or other symbols to answers so that the responses can be grouped into limited number of classes or categories. The code includes an indication of the column and data record it will occupy. For eg. Sex of respondents may be coded as 1for males and 2 for females. Questions Answers Codes 1. Do you own a vehicle? Yes 1 No 2 2. What is your occupation? Salaried S Business B Retired R
  • 9. Tabulation Refers to counting the number of cases that fall into various categories. The results are summarized in the form of statistical tables. The raw data is divided into groups and sub-groups. The counting and placing of data in a particular group and sub-group are done. The tabulation involves: 1. Sorting and counting. 2. Summarising of data. Tabulation may be of two types: 1. Simple tabulation In simple tabulation, a single variable is counted. 2. Cross tabulation Includes two or more variables, which are treated simultaneously. Tabulation can be done entirely by hand, or by machine, or by both hand and machine.
  • 10. Sorting and counting of data: Sorting can be done as follows: Format of a Blank table Table No. TITLE Number of children per family Head Note Unit of measurement Income (Rs) Tally Marks Frequencies 1000 IIII 4 1500 II 2 2000 III 3 Sub- Headin g Caption Body Foot note Total Sub heading indicates the row title or the row headings. Caption indicates what each column is meant for. Body of the table gives full information of
  • 11. Kinds of Tabulation 1. Simple or one-way tabulation The multiple choice questions which allow only one answer may use on-way tabulation or univariate. The questions are predetermined and consist of counting the number of responses falling into a particular category and calculate the percentage. Example Table 14.1: Study of number of children in a family No. of children Family Percentage 0 10 5 1 30 15 2 70 35
  • 12. 2. Cross Tabulation or Two-way Tabulation This is known as Bivariate Tabulation.The data may include two or more variables. Eg. Popularity of a health drink among families having different incomes. Table 14.3: Use of Health Drink Income per month No. of children per family (0) 1 2 No. of families 1000 10 5 8 23 1001-2000 5 0 8 13 2001-3000 20 10 12 42
  • 13. Data cleaning Includes consistency checks and treatment of missing responses. Although preliminary consistency checks have been made during editing, the checks at this stage are more thorough and extensive, because they are made by computer. Consistency checks Identify data that are out of range, logically inconsistent or have extreme values. For eg. A respondent may indicate that she charges long distance calls to a calling card, although she does not have one. http://www.facebook.com/mr.fortyseven
  • 14. Treatment of missing responses Missing responses represent values of a variable that are unknown, either because respondents provided ambiguous answers or their answers were not properly recorded. 1. Substitute a Neutral Value A neutral value, typically the mean to the variable, is substituted for the missing responses. 2. Substitute an Imputed Response The respondents pattern of responses to other questions are used to impute or calculate a suitable response to the missing questions. 3. Casewise Deletion Cases or respondents with any missing responses are discarded from the analysis. 4. Pairwise deletion Instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation. As a result, different calculations in an analysis may be based on different sample sizes. http://www.facebook.com/mr.fortyseven
  • 15. Statistically Adjusting the Data If any correction needs to be done for the statistical analysis, the data is adjusted accordingly. Selecting a Data Analysis Strategy The selection of a data analysis strategy should be based on the earlier steps of the marketing research process, known characteristics of the data, properties of statistical techniques and the background and philosophy of the researcher. http://www.facebook.com/mr.fortyseven