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

John Wiley & Son, Inc. 2 1.To develop an understanding of the importance and nature of quality control checks. 2.To understand the data entry process and data entry alternatives. 3.To learn how surveys are tabulated and crosstabulated. 4.To understand the concept of hypothesis development and how to test hypotheses Learning Objectives

John Wiley & Son, Inc. 3 The Data Analysis Procedure To develop an understanding of the importance and nature of quality control checks 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

John Wiley & Son, Inc. 4 Step One: Validation and Editing Validation –The process of ascertaining that interviews actually were conducted as specified. –Telephone Validation Was the person actually interviewed? Was the respondent actually qualified? Was the interview conducted in the required manner? Did the interviewer cover the entire survey? –Check for other types of problems –Purpose of the Validation To develop an understanding of the importance and nature of quality control checks

John Wiley & Son, Inc. 5 Editing Checking for interviewer and respondent mistakes Editing Process 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 develop an understanding of the importance and nature of quality control checks Step One: Validation and Editing

John Wiley & Son, Inc. 6 Step Two: Coding 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 develop an understanding of the importance and nature of quality control checks

John Wiley & Son, Inc. 7 Step Three: Data Entry Data Entry –Process of converting information to a form that can be read by a computer 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. To understand the data-entry process and data-entry alternatives.

John Wiley & Son, Inc. 8 To understand the data-entry process and data-entry alternatives. Scanning –Optical Scanning –Electronically Captured Data is Increasing Computer-assisted telephone interviewing Internet surveys Disks-by-mail surveys TouchScreen Kiosk surveys Step Three: Data Entry

John Wiley & Son, Inc. 9 Step Four: Machine Cleaning of Data Machine Cleaning of Data –A final computerized error check of data. Error Checking Routines –Check for logical errors in the 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. Final Error Check in the Process –Should be ready for tabulation and statistical analysis To understand the data-entry process and data-entry alternatives.

John Wiley & Son, Inc. 10 Step Five: Tabulation and Statistical Analysis To learn how surveys are tabulated and cross-tabulated One Way Frequency Tables –A table showing the number of responses to each answer. –The first summary of survey results Options for Base of the 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

John Wiley & Son, Inc. 11 Cross-Tabulations –Examination of the responses of one question relative to responses to one or more other questions. –Three different percentages calculated for each cell in a crosstabulation table Column percentage Row percentage Total percentages Step Five: Tabulation and Statistical Analysis To learn how surveys are tabulated and cross-tabulated

John Wiley & Son, Inc. 12 Graphic Representations of Data Line Charts –The simplest form of graphs. Pie Charts –Appropriate for displaying marketing research results in a wide range of situations. Bar Charts 1. Plain bar chart 2. Clustered bar charts 3. Stacked bar charts 4. Multiple row, three-dimensional bar charts Examples follow slides 13-19

John Wiley & Son, Inc. 13 Exhibit Line Chart

John Wiley & Son, Inc. 14 Exhibit Pie Chart

John Wiley & Son, Inc. 15 Exhibit Simple Two Dimensional Bar Cart

John Wiley & Son, Inc. 16 Exhibit Simple Three Dimensional Bar Chart

John Wiley & Son, Inc. 17 Exhibit Clustered Bar Chart

John Wiley & Son, Inc. 18 Exhibit Stacked Bar Chart

John Wiley & Son, Inc. 19 Exhibit Multiple-Row, Three Dimensional Bar Chart

John Wiley & Son, Inc. 20 Descriptive Statistics Measures of Central Tendency –Nominal and Ordinal Scales –Interval and Ratio Scales –Mean –Median –Mode

John Wiley & Son, Inc. 21 Measures of Central Tendency Formula for the Mean 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 Descriptive Statistics

John Wiley & Son, Inc. 22 Measures of Dispersion –Standard deviation –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 Descriptive Statistics

John Wiley & Son, Inc. 23 Measures of Dispersion Standard deviation 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 Descriptive Statistics

John Wiley & Son, Inc. 24 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 tables and crosstabulations are used for analysis –Continuous data can be put into categories. Evaluating Differences and Changes Descriptive Statistics

John Wiley & Son, Inc. 25 Statistical Significance Statistical Inference –To generalize from sample results to population characteristics Three Concepts of Differences –Mathematical differences –Statistical significance –Managerially important differences

John Wiley & Son, Inc. 26 To understand the concept of hypothesis development and how to test hypotheses. Hypothesis Testing Hypothesis –An assumption that a researcher makes about some characteristic of the population under study. Explanation for Differences between a Hypothesized Value and a Particular Research Result –The Hypothesis is true and the observed difference is likely due to sampling error –The Hypothesis is false and the true value is some other value

John Wiley & Son, Inc. 27 Steps in Hypothesis Testing –Step One: Stating the Hypothesis Null hypothesis: H o Alternative hypothesis: H a –Step Two: Choosing the Appropriate Test Statistic Exhibit Statistical Tests and Their Uses— provides a guide to selecting the appropriate test for various situations To understand the concept of hypothesis development and how to test hypotheses. Hypothesis Testing

John Wiley & Son, Inc. 28 –Step Three: Developing a Decision Rule Significance level (α)—0.01, 0.05, or 0.10—that will determine whether to reject or fail to reject the null hypothesis –Step Four: Calculating the Value of the Test Statistic Use the appropriate formula Compare calculated value to the critical value. State the result in terms of: –rejecting the null hypothesis –failing to reject the null hypothesis –Step Five: Stating the Conclusion Summarizes the results of the test—should be stated from the perspective of the original research question To understand the concept of hypothesis development and how to test hypotheses. Hypothesis Testing

John Wiley & Son, Inc. 29 Types of Errors in Hypothesis Testing –Type I Error Rejection of the null hypothesis when, in fact, it is true. 1 – α is the probability of making a correct decision by not rejecting the null hypothesis when, in fact, it is true –Type II Error Acceptance of the null hypothesis when, in fact, it is false. 1– β reflects the probability of making a correct decision in rejecting the null hypothesis when, in fact, it is false –Accepting Ho or Failing to Reject Ho? Is there enough data to conclude that H o is correct One-Tailed Test or Two-Tailed Test? Hypothesis Testing To understand the concept of hypothesis development and how to test hypotheses.

John Wiley & Son, Inc. 30 Type I and Type II Errors Actual State of the Null Hypothesis Fail to Reject H o Reject H o H o is true H o is false Correct (1-  ) no error Type II error (  ) Type I error (  ) Correct (1-  ) no error Table 12.21

John Wiley & Son, Inc. 31 Commonly Used Statistical Hypothesis Tests Independent Versus Related Samples – Independent samples Measurement of a variable in one population has no effect on the measurement of the other variable – Related Samples Measurement of a variable in one population may influence the measurement of the other variable. Degrees of Freedom –The number of observations minus the number of constraints. –The number of degrees of freedom To understand the concept of hypothesis development and how to test hypotheses.

John Wiley & Son, Inc. 32 p - VALUES AND SIGNIFICANCE TESTING –P- Value The exact probability of getting a computed test statistic that was largely due to chance 1.The smaller the p-value, the smaller the probability that the observed result occurred by chance. 2.The p-value is the demanding level of statistical significance that can be met, based on the calculated value of the statistic To understand the concept of hypothesis development and how to test hypotheses. Commonly Used Statistical Hypothesis Tests

John Wiley & Son, Inc. 33 Overview of the Data Analysis Procedure Step One: Validation and Editing Step Two: Coding Step Three: Data Entry Step Four: Machine Cleaning of Data Step Five: Tabulation and Statistical Analysis Graphic Representations of Data Descriptive Statistics SUMMARY

John Wiley & Son, Inc. 34 Statistical Significance Hypothesis Testing Commonly Used Statistical Hypothesis Tests P-Values and Significance Testing Statistics on the Internet SUMMARY

John Wiley & Son, Inc. 35 The End Copyright © 2006 John Wiley & Son, Inc