16-1 Chapter 16 Data Preparation andDescription
16-2 Learning Objectives Understand... importance of editing the collected raw data to detect errors and omissions how coding is used to assign number and other symbols to answers and to categorize responses use of content analysis to interpret and summarize open questions
16-3 Learning Objectives Understand... problems and solutions for “don’t know” responses and handling missing data options for data entry and manipulation
16-4 Exhibit 16-1 Data Preparation in the Research Process
16-5 Editing Criteria Consistent with the intent of Ques Consistent with the intent of Ques Uniformly entered Uniformly entered Arranged for simplification Arranged for simplification Complete Accurate
16-6 Field Editing Field editing review Abbreviations to be clarified Re interview some Entry gaps identified Callbacks made Validate results
16-7 Central Editing 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
16-8 Central Editing Incorrect entries are corrected In appropriate or missing replies Armchair Interviewing can be detected
16-9 Coding Coding involves assigning numbers or other symbols to answers so that the response can be grouped into a limited number of categories Categorisation is the process of using rules to partition a body of data
16-10 Codebook Construction Code book or coding scheme contains each variable in the study and specifies the application of coding rules to the variable Used to promote more accurate and more efficient data entry
16-11 Coding Closed Questions Easier to code, record and analyse Precoding
16-12 Coding Free Response Open ended responses are used for measuring sensitive or disapproved behaviour, encourage natural modes of expression Slows the analysis process and increases the opportunity for error
16-13
16-14 Exhibit 16-2 Sample Codebook
16-15 Exhibit 16-3 Precoding
16-16 Exhibit 16-3 Coding Open- Ended Questions
16-17 Coding Rules Categories should be Categories should be Appropriate to the research problem Exhaustive Mutually exclusive Derived from one classification principle
16-18 Coding Rules Appropriateness –Best partitioning of the data for testing hypotheses and showing relationships –Availability of comparison data Exhaustiveness –Does the response cover all the possibilities –Others may be classified further
16-19 Coding Rules Mutual Exclusivity –Example of profession –Add more field of second profession Derived from one classification principle –Manager and Unemployed
16-20 Content Analysis QSR’s XSight software for content analysis.
16-21 Types of Content Analysis Syntactical Propositional Referential Thematic
16-22 Content Analysis Syntactical –Words, phrases, sentences, or paragraphs –What is the meaning they reveal Referential –Units described by words or phrases, they may be objects, events, persons
16-23 Content Analysis Propositional units –Are assertions about an object, event, person and so on Thematic –Past, present or the future in responses
16-24 Example How can company – customer relations be improved ?
16-25 Exhibit 16-4 & 16-5 Open-Question Coding Locus of Responsibility Mentioned Not Mentioned A. Company________________________ B. Customer________________________ C. Joint Company-Customer________________________ F. Other________________________ Locus of ResponsibilityFrequency (n = 100) A. Management 1. Sales manager 2. Sales process 3. Other 4. No action area identified B. Management 1. Training C. Customer 1. Buying processes 2. Other 3. No action area identified D. Environmental conditions E. Technology F. Other
16-26 Don’t Know Responses Design better questions in the beginning Good rapport will ensure less DK responses –Who developed the Managerial grid concept –Do you believe the current budget is good –Do you like your present job –How often each year you go to the movies
16-27 Exhbit 16-7 Handling “Don’t Know” Responses Question: Do you have a productive relationship with your present salesperson? Years of Purchasing YesNoDon’t Know Less than 1 year10%40%38% 1 – 3 years years or more6030 Total 100% n = % n = % n = 200
16-28 Data Entry Database Programs Database Programs Optical Recognition Optical Recognition Digital/ Barcodes Digital/ Barcodes Voice recognition Voice recognition Keyboarding
16-29 Missing Data Listwise Deletion Pairwise Deletion Replacement
16-30 Key Terms Bar code Codebook Coding Content analysis Data entry Data field Data file Data preparation Database Don’t know response Editing Missing data Optical character recognition Optical mark recognition Precoding Record Spreadsheet Voice recognition
16-31 Appendix 16a Describing Data Statistically
16-32 Frequencies Unit Sales Increase (%)FrequencyPercentage Cumulative Percentage Total Unit Sales Increase (%)FrequencyPercentage Cumulative Percentage Origin, foreign (1) Origin, foreign (2) Total A B
16-33 Distributions
16-34 Characteristics of Distributions
16-35 Measures of Central Tendency MeanModeMedian
16-36 Measures of Variability Interquartile range Quartile deviation Quartile deviation Dispersion Range Standard deviation Variance
16-37 Summarizing Distributions with Shape
16-38 _ _ _ Symbols
16-39 Key Terms Central tendency Descriptive statistics Deviation scores Frequency distribution Interquartile range (IQR) Kurtosis Median Mode Normal distribution Quartile deviation (Q) Skewness Standard deviation Standard normal distribution Standard score (Z score) Variability Variance