Lecture Marketing research - Chapter 12: Data Processing, fundamental data analysis, and statistical testing of differences

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In this chapter you will: Develop an understanding of the importance and nature of quality control checks, understand the data entry process and data entry alternatives, learn how surveys are tabulated and cross-tabulated, understand the concept of hypothesis development and how to text hypotheses.

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Nội dung Text: Lecture Marketing research - Chapter 12: Data Processing, fundamental data analysis, and statistical testing of differences

Learning Objectives CHAPTER Twelve Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences Copyright © 2004 John Wiley & Sons, Inc.
Learning Objectives Learning Objectives 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 cross- tabulated. 4. To understand the concept of hypothesis development and how to text hypotheses.
Learning Objectives To get an overview of the data The Data Analysis Procedure 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
Learning Objectives Validation and Editing To understand the importance and nature of quality control checks. 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.
Learning Objectives Data Entry To understand the data-entry process and data-entry alternatives. 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.
Learning Objectives Tabulation of To learn how surveys are tabulated. Survey Results 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
Learning Objectives Tabulation of To learn how to set up and Survey Results interpret crosstabulations. 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.
Learning Objectives Graphic Representations To comprehend the basic of Data techniques of statistical analysis. 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
Learning Objectives To comprehend the basic Descriptive Statistics techniques of statistical analysis. Measures of Central Tendency • Mean h fiXi I=1 X = where n fi = the frequency of the ith class Xi = the midpoint of that class h = the number of classes n = the total number of observations
Learning Objectives To comprehend the basic Descriptive Statistics techniques of statistical analysis. • Mean The sum of the values for all observation of a variable divided by the number of observations • Median The observation below which 50 percent of the observations fall. • Mode The value that occurs most frequently
Learning Objectives To comprehend the basic Descriptive Statistics techniques of statistical analysis. 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
Learning Objectives To comprehend the basic Descriptive Statistics techniques of statistical analysis. 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.
Learning Objectives To comprehend the basic Descriptive Statistics techniques of statistical analysis. Measures of Dispersion Standard deviation (continued) √ n (Xi - X) 2 S = I=1 where n-1 S = sample standard deviation Xi = the value of the ith observation X = the sample mean n = the sample size
Learning Objectives To comprehend the basic Descriptive Statistics techniques of statistical analysis. 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.
Learning Objectives Differences and Changes To become aware of the nature of statistical differences. Are certain measures different from one another? For example: Did top-of-mind awareness really increase? Did customer satisfaction really increase?
Learning Objectives Statistical Significance To become aware of the nature of statistical differences. It is possible for numbers to be different in a mathematical sense but not statistically different in a statistical sense. • Mathematical differences • Statistical significance • Managerially important differences
Learning Objectives To understand the concept of Hypothesis Testing hypothesis development and how to test hypotheses. Hypothesis An assumption that a researcher makes about some characteristic of the population under study. Steps in Hypothesis Testing Step One: Stating the Hypothesis Null hypothesis: Ho Alternative hypothesis: Ha Step Two: Choosing the Appropriate Test Statistic
Learning Objectives To understand the concept of Hypothesis Testing hypothesis development and how to test hypotheses. Step Three: Developing a Decision Rule 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
Learning Objectives To understand the differences Other issues between Type I and Type II errors. Types of Errors in Hypothesis Testing Type I Error Rejection of the null hypothesis when, in fact, it is true. Type II Error Acceptance of the null hypothesis when, in fact, it is false. Accepting Ho or Failing to Reject Ho? One-Tailed Test or Two-Tailed Test?
Learning Objectives Table 12.13 Type I and Type II Errors Actual State of the Fail to Reject Ho Reject Ho Null Hypothesis Ho is true Correct (1- ) Type I error ( ) no error Ho is false Type II error ( ) Correct (1- ) no error