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

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.

Copyright © 2004 John Wiley & Sons, Inc.

Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences

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.

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

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

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.

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.

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

1 Plain bar chart

2 Clustered bar charts

3 Stacked bar charts

4 Multiple row, three-dimensional bar charts

Measures of Central Tendency

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.

The value that occurs most frequently

To comprehend the basic techniques of statistical analysis.

Descriptive Statistics

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

• 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

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

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

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

• Managerially important differences

To become aware of the nature

of statistical differences.

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: HoAlternative hypothesis: Ha

Step Two: Choosing the Appropriate Test Statistic

hypothesis development and how to test hypotheses.

Hypothesis Testing

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

hypothesis development and how to test hypotheses.

Hypothesis Testing

Types of Errors in Hypothesis Testing

Accepting Ho or Failing to Reject Ho?

One-Tailed Test or Two-Tailed Test?

between Type I and Type II errors.

Other issues

Type I and Type II Errors

Actual State of the

Independent Versus Related Samples

Independent samples

Measurement of a variable in one population has

no effect on the measurement of the other variable

Commonly Used

Statistical Hypothesis Tests

• Validation and Editing

• Data Entry

• Optical Scanning

• Machine Cleaning of Data

• Tabulation of Survey Results

• Graphic Representations of Data

• Descriptive Statistics

SUMMARY

• Differences and Changes

• Statistical Significance

• Hypothesis Testing

SUMMARY

The End

Copyright © 2004 John Wiley & Sons, Inc.