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

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Learning Objectives 2 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.

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Learning Objectives 3 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

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Learning Objectives 4 Step 1: Validation The process of ascertaining that interviews actually were conducted as specified (e.g., proper screening, proper procedures followed) Step 1: 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. Step 1: Validation and Editing

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Learning Objectives 5 Step 2: Coding 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. Step 2: Coding

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Learning Objectives 6 Step 3: Intelligent Data Entry Information being entered checked for internal logic. The Data Entry 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. Optical Scanning A data processing device that can “read”response questionnaires Step 3: Data Entry To understand the data-entry process and data-entry alternatives.

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Learning Objectives 7 Optical Scanning To understand the data-entry process and data-entry alternatives. Step 4: 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.

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Learning Objectives 8 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 Step 5: Tabulation of Survey Results To learn how surveys are tabulated.

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Learning Objectives 9 To learn how to set up and interpret crosstabulations. Tabulation of Survey Results Crosstabulations 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.

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Learning Objectives 10 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 (best for proportional relationships) 2. Clustered bar charts 3. Stacked bar charts 4. Multiple row, three-dimensional bar charts (best for crosstabulations)

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Learning Objectives 11 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.

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Learning Objectives 12 Measures of Central Tendency 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

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Learning Objectives 13 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

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Learning Objectives 14 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

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Learning Objectives 15 Measures of Dispersion Variance 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

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Learning Objectives 16 Means, Percentages, and Statistical Tests Choice: Whether to use measures of central tendency or percentages. Responses are either categorical (e.g., 1 = Atlanta, 2 = NY, etc.) or take the form of continuous variables (e.g., weight) (Variables such as age can be continuous or categorical.) For categories, one-way frequency distributions and crosstabulations are the most obvious choices. Continuous data can be put into categories (and means calculated) To comprehend the basic techniques of statistical analysis. Descriptive Statistics

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

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

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Chapter Twelve Copyright © 2006 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences.

Chapter Twelve Copyright © 2006 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences.

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