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Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences CHAPTER.

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Presentation on theme: "Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences CHAPTER."— Presentation transcript:

1 Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences CHAPTER Twelve

2 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.

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 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.

6 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.

7 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

8 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

9 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.

10 Learning Objectives 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 To comprehend the basic techniques of statistical analysis. Descriptive Statistics

11 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

12 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

13 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

14 Learning Objectives 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

15 Learning Objectives Are certain measures different from one another? For example: Did top-of-mind awareness really increase? Did customer satisfaction really increase? To become aware of the nature of statistical differences. Differences and Changes

16 Learning Objectives Statistical Significance 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 To become aware of the nature of statistical differences.

17 Learning Objectives 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: H o Alternative hypothesis: H a Step Two: Choosing the Appropriate Test Statistic To understand the concept of hypothesis development and how to test hypotheses. Hypothesis Testing

18 Learning Objectives 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 To understand the concept of hypothesis development and how to test hypotheses. Hypothesis Testing

19 Learning Objectives 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 H o or Failing to Reject H o ? One-Tailed Test or Two-Tailed Test? To understand the differences between Type I and Type II errors. Other issues

20 Learning Objectives Table 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

21 Learning Objectives 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. To understand the concept of hypothesis development and testing a hypothesis. Commonly Used Statistical Hypothesis Tests

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

23 Learning Objectives Differences and Changes Statistical Significance Hypothesis Testing SUMMARY

24 Learning Objectives The End Copyright © 2004 John Wiley & Sons, Inc.


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