1. Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Processing and Fundamental Data Analysis CHAPTER fourteen

2. Learning Objectives 1. To get an overview of the data analysis procedure. 2. To develop an understanding of the importance and nature of quality control checks. 3. To understand the data entry process and data entry alternatives. 4. To learn how surveys are tabulated and crosstabulated. 5. To learn how to set up and interpret crosstabulations. 6.To comprehend the basic techniques of statistical analysis.

3. Learning Objectives To get an overview of the data analysis procedure. The Data Analysis Procedure Five Step Procedure for Data Analysis: Step One: Validation and editing (quality control) Step Two: Coding Step Three: Data Entry Step Four: Machine Cleaning of Data Step Five: Tabulation and Statistical Analysis

4. Learning Objectives Validation The process of ascertaining that interviews actually were conducted as specified. Editing Checking for interviewer mistakes 1. Did the interviewer ask or record answers for certain questions? 2. Questionnaires are checked to make sure Skip patterns are followed. 3. Responses to open-ended responses are checked. To understand the importance and nature of quality control checks. Validation and Editing

5. Learning Objectives Coding Defined Grouping and assigning numeric codes to the responses The Coding Process 1. Listing responses 2. Consolidating responses 3. Setting codes 4. Entering codes To understand the data-entry process and data-entry alternatives. Coding

6. Learning Objectives Intelligent Data Entry The checking of information being entered for internal logic by either that data entry device or another device connected to it. The Data Entry Process The mechanics of the process. The validated, edited, and coded questionnaires are given to a data entry operator. The process of going directly from the questionnaire to the data entry device and storage medium is more accurate and efficient. Data Entry To understand the data-entry process and data-entry alternatives.

7. Learning Objectives Optical Scanning A data processing device that can “read”response questionnaires Optical Scanning To understand the data-entry process and data-entry alternatives. Machine Cleaning of Data A final computerized error check of data. Marginal Report A computer-generated table of the frequencies of the responses to each question to monitor entry of valid codes and correct use of skip patterns.

8. Learning Objectives One Way Frequency Tables A table showing the number of responses to each answer. Base for Percentages 1. Total respondents 2. Number of people asked the question 3. Number of people answering the question Selecting the Base for One-Way Frequency Tables Showing Results from Multiple-Choice Questions Tabulation of Survey Results To learn how surveys are tabulated.

9. Learning Objectives Cross-Tabulations Examination of the responses of one question relative to responses to one or more other questions. Provides a powerful and easily understood approach to the summarization and analysis of survey research results. To learn how to set up and interpret crosstabulations. Tabulation of Survey Results

10. Learning Objectives Line Charts The simplest form of graphs. Pie Charts Appropriate for displaying marketing research results in a wide range of situations. Graphic Representations of Data To comprehend the basic techniques of statistical analysis. Bar Charts 1. Plain bar chart 2. Clustered bar charts 3. Stacked bar charts 4. Multiple row, three-dimensional bar charts

11. Learning Objectives Measures of Central Tendency Mean Descriptive Statistics X  h I = 1 n fiXifiXi = where f i = the frequency of the ith class X i = the midpoint of that class h = the number of classes n = the total number of observations To comprehend the basic techniques of statistical analysis.

12. Learning Objectives Median The observation below which 50 percent of the observations fall. Mode The value that occurs most frequently To comprehend the basic techniques of statistical analysis. Descriptive Statistics

13. Learning Objectives Measures of Dispersion Standard deviation Calculated by: subtracting the mean of a series from each value in a series squaring each result summing them dividing by the number of items minus 1 and taking the square root of this value. To comprehend the basic techniques of statistical analysis. Descriptive Statistics

14. Learning Objectives Measures of Dispersion Standard deviation (continued) S  n I = 1 n - 1 (X i - X) 2 = √ where S = sample standard deviation X i = the value of the ith observation X = the sample mean n = the sample size To comprehend the basic techniques of statistical analysis. Descriptive Statistics

15. Learning Objectives Measures of Dispersion Variance The sums of the squared deviations from the mean divided by the number of observations minus one. The same formula as standard deviation with the square-root sign removed. Range The maximum value for a variable minus the minimum value for that variable To comprehend the basic techniques of statistical analysis. Descriptive Statistics

16. Learning Objectives Means, Percentages, and Statistical Tests Whether to use measures of central tendency or percentages. Responses are either categorical or take the form of continuous variables Variables such as age can be continuous or categorical. If categories are used, one-way frequency distributions and crosstabulations are the most obvious choices. Continuous data can be put into categories. To comprehend the basic techniques of statistical analysis. Descriptive Statistics

17. Learning Objectives Validation and Editing Coding Data Entry Optical Scanning Machine Cleaning of Data Tabulation of Survey Results Graphic Representations of Data Descriptive Statistics SUMMARY

18. Learning Objectives The End Copyright © 2002 South-Western/Thomson Learning