Data Preparation and Processing
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The document outlines the process of data preparation and processing, emphasizing the importance of validation, coding, and cleaning of data collected through questionnaires. It details the steps involved in preparing data for analysis, including checking and editing questionnaires, coding responses, and managing unsatisfactory data. Additionally, the document discusses statistical adjustments and techniques for analyzing data, providing insights into ensuring data quality and representativeness.
Data Preparation and Processing
- 2. 2 DATA PREPARATION • Once data is collected, process of analysis begins. • But, data has to be translated in an appropriate form. • This process is known as Data Preparation
- 3. 3 STEPS IN DATA PREPARATION • Validate data • Questionnaire checking • Edit acceptable questionnaires • Code the questionnaires • Keypunch the data • Clean the data set • Statistically adjust the data • Store the data set for analysis • Analyse data
- 4. 4 VALIDATION • Validity exists when the data actually measure what they are suppose to measure. If they fail to, they are misleading and should not be accepted. • One of the most serious concerns is errors in survey data. • When secondary data are involved, they may be ancient or unimportant. • With primary data also, this review is important.
- 5. 5 QUESTIONNAIRE CHECKING • A questionnaire returned from the field may be unacceptable for several reasons. –Parts of the questionnaire may be incomplete. Inadequate answers. No responses to specific questions –The pattern of responses may indicate that the respondent did not understand or follow the instructions. –The responses show little variance. –One or more pages are missing.
- 6. 6 QUESTIONNAIRE CHECKING –The questionnaire is answered by someone who does not qualify for participation. –Fictitious interviews –Inconsistencies –Illegible responses –Yea- or nay-saying patterns –Middle-of-the-road patterns
- 7. 7 EDITING • Next phase of data preparation involves editing of the raw data. • Three basic approaches: - Go back to the respondents for clarification - Infer from other responses - Discard the response altogether
- 8. 8 Treatment of Unsatisfactory Responses Treatment of Unsatisfactory Responses Return to the Field Discard Unsatisfactory Respondents Assign Missing Values Substitute a Neutral Value Casewise Deletion Pairwise Deletion
- 9. 9 Treatment of Unsatisfactory Results: - Returning to the Field – The questionnaires with unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents. - Assigning Missing Values – If returning the questionnaires to the field is not feasible, the editor may assign missing values to unsatisfactory responses. - Discarding Unsatisfactory Respondents – In this approach, the respondents with unsatisfactory responses are simply discarded
- 10. 10 CODING • Data entry refers to the creation of a computer file that holds the raw data taken from all of the questionnaires deemed suitable for analysis • Coding means assigning a code, usually a number, to each possible response to each question. The code includes an indication of the column position (field) and data record it will occupy.
- 11. 11 CODING • Fixed field codes, which mean that the number of records for each respondent is the same and the same data appear in the same column(s) for all respondents, are highly desirable. –If possible, standard codes should be used for missing data. Coding of structured questions is relatively simple, since the response options are predetermined.
- 12. 12 CODING –In questions that permit a large number of responses, each possible response option should be assigned a separate column. –Guidelines for coding unstructured questions: – Category codes should be mutually exclusive and collectively exhaustive. – Only a few (10% or less) of the responses should fall into the “other” category. – Category codes should be assigned for critical issues even if no one has mentioned them. – Data should be coded to retain as much detail as possible.
- 13. 13 CODING • Principles for establishing categories for coding: - Convenient number of categories - Similar responses within categories - Differences of responses between categories - Mutually exclusive categories - Exhaustive categories - Avoid open-ended class intervals - Class interval of the same width - Midpoints of class intervals
- 14. 14 CODE BOOK • A codebook contains coding instructions and the necessary information about variables in the data set. A codebook generally contains the following information: - column number - record number - variable number - variable name - question number - instructions for coding
- 15. 15 CODE BOOK • Thus, a Data code book identifies all of the variable names and code numbers associated with each possible response to each question that makes up the data set
- 16. 16 Restaurant Preference ID PREFER. QUALITY QUANTITY VALUE SERVICE INCOME 1 2 2 3 1 3 6 2 6 5 6 5 7 2 3 4 4 3 4 5 3 4 1 2 1 1 2 5 5 7 6 6 5 4 1 6 5 4 4 5 4 3 7 2 2 3 2 3 5 8 3 3 4 2 3 4 9 7 6 7 6 5 2 10 2 3 2 2 2 5 11 2 3 2 1 3 6 12 6 6 6 6 7 2 13 4 4 3 3 4 3 14 1 1 3 1 2 4 15 7 7 5 5 4 2 16 5 5 4 5 5 3 17 2 3 1 2 3 4 18 4 4 3 3 3 3 19 7 5 5 7 5 5 20 3 2 2 3 3 3
- 17. 17 A Codebook Excerpt Column Number Variable Number Variable Name Question Number Coding Instructions 1 1 ID 1 to 20 as coded 2 2 Preference 1 input the number circled. 1=Weak Preference 7=Strong Preference 3 3 Quality 2 Input the number circled. 1=Poor 7=Excellent 4 4 Quantity 3 Input the number circled. 1=Poor 7=Excellent
- 18. 18 A Codebook Excerpt Column Number Variable Number Variable Name Question Number Coding Instructions 5 5 Value 4 Input the number circled. 1=Poor 7=Excellent 6 6 Service 5 Input the number circled. 1=Poor 7=Excellent 7 7 Income 6 Input the number circled. 1 = Less than $20,000 2 = $20,000 to 34,999 3 = $35,000 to 49,999 4 = $50,000 to 74,999 5 = $75,000 to 99,999 6 = $100,00 or more
- 19. 19 SPSS Variable View of the Data of Table
- 20. 20 Keypunch the data / Data transcription • Transcribing data is the process of transferring the coded data from the questionnaire or coding sheets onto disks or magnetic tapes or directly into computers by keypunching.
- 21. 21 Keypunch the data / Data transcription Transcribed Data CATI / CAPI Keypunching via CRT Terminal Optical Scanning Mark Sense Forms Computerized Sensory Analysis Verification:Correct Keypunching Errors Disks Magnetic Tapes Computer Memory Raw Data
- 22. 22 Data Cleaning • Consistency Checks - Consistency checks identify data that are out of range, logically inconsistent, or have extreme values. - Computer packages like SPSS, SAS, EXCEL and MINITAB can be programmed to identify out-of- range values for each variable and print out the respondent code, variable code, variable name, record number, column number, and out-of-range value. - Extreme values should be closely examined.
- 23. 23 Data Cleaning • Treatment of Missing Responses • Substitute a Neutral Value – A neutral value, typically the mean response to the variable, is substituted for the missing responses. • 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. • In case wise deletion, cases, or respondents, with any missing responses are discarded from the analysis. • In pair wise deletion, instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation.
- 24. 24 Statistically Adjusting the Data • Weighting • In weighting, each case or respondent in the database is assigned a weight to reflect its importance relative to other cases or respondents. • Weighting is most widely used to make the sample data more representative of a target population on specific characteristics. • Yet another use of weighting is to adjust the sample so that greater importance is attached to respondents with certain characteristics.
- 25. 25 Statistically Adjusting the Data Use of Weighting for Representativeness Years of Sample Population Education Percentage Percentage Weight Elementary School 0 to 7 years 2.49 4.23 1.70 8 years 1.26 2.19 1.74 High School 1 to 3 years 6.39 8.65 1.35 4 years 25.39 29.24 1.15 College 1 to 3 years 22.33 29.42 1.32 4 years 15.02 12.01 0.80 5 to 6 years 14.94 7.36 0.49 7 years or more 12.18 6.90 0.57 Totals 100.00 100.00
- 26. 26 Statistically Adjusting the Data • Variable Respecification • Variable respecification involves the transformation of data to create new variables or modify existing variables. • E.G., the researcher may create new variables that are composites of several other variables. • Dummy variables are used for respecifying categorical variables. The general rule is that to respecify a categorical variable with K categories, K-1 dummy variables are needed
- 27. 27 Statistically Adjusting the Data Product Usage Original Dummy Variable Code Category Variable Code X1 X2 X3 Nonusers 1 1 0 0 Light users 2 0 1 0 Medium users 3 0 0 1 Heavy users 4 0 0 0 Note that X1 = 1 for nonusers and 0 for all others. Likewise, X2 = 1 for light users and 0 for all others, and X3 = 1 for medium users and 0 for all others. In analyzing the data, X1, X2, and X3 are used to represent all user/nonuser groups
- 28. 28 Statistically Adjusting the Data • Scale Transformation and Standardization: - Scale transformation involves a manipulation of scale values to ensure comparability with other scales or otherwise make the data suitable for analysis. - A more common transformation procedure is standardization. Standardized scores, Zi, may be obtained as: Zi = (Xi - )/sxX
- 29. 29 A Classification of Univariate Techniques Independent Related Independent Related * Two- Group test * Z test * One-Way ANOVA * Paired t test * Chi-Square * Mann-Whitney * Median * K-S * K-W ANOVA * Sign * Wilcoxon * McNemar * Chi-Square Metric Data Non-numeric Data Univariate Techniques One Sample Two or More Samples One Sample Two or More Samples * t test * Z test * Frequency * Chi-Square * K-S * Runs * Binomial
- 30. 30 A Classification of Multivariate Techniques More Than One Dependent Variable * Multivariate Analysis of Variance and Covariance * Canonical Correlation * Multiple Discriminant Analysis * Cross- Tabulation * Analysis of Variance and Covariance * Multiple Regression * Conjoint Analysis * Factor Analysis One Dependent Variable Variable Interdependence Interobject Similarity * Cluster Analysis * Multidimensional Scaling Dependence Technique Interdependence Technique Multivariate Techniques