chapter 20 basic data analysis: descriptive statistics © 2010 south-western/cengage learning. all...

Chapter 20Chapter 20Basic Data Basic Data Analysis: Analysis:

Descriptive Descriptive StatisticsStatistics

© 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

© 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

ZIKMUND BABINCARR GRIFFIN

BUSINESS MARKET RESEARCH

EIGHTH EDITION

© 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part. 20–2

LEARNING OUTCOMESLEARNING OUTCOMESLEARNING OUTCOMESLEARNING OUTCOMES

1.1. Know what descriptive statistics are and why they are Know what descriptive statistics are and why they are usedused

2.2. Create and interpret simple tabulation tablesCreate and interpret simple tabulation tables

3.3. Understand how cross-tabulations can reveal Understand how cross-tabulations can reveal relationshipsrelationships

4.4. Perform basic data transformationsPerform basic data transformations

5.5. List different computer software products designed for List different computer software products designed for descriptive statistical analysisdescriptive statistical analysis

6.6. Understand a researcher’s role in interpreting the dataUnderstand a researcher’s role in interpreting the data

After studying this chapter, you should be able to


The Nature of Descriptive AnalysisThe Nature of Descriptive Analysis

• Descriptive AnalysisDescriptive Analysis The elementary transformation of raw data in a way The elementary transformation of raw data in a way

that describes the basic characteristics such as that describes the basic characteristics such as central tendency, distribution, and variability.central tendency, distribution, and variability.

• HistogramHistogram A graphical way of showing a frequency distribution in A graphical way of showing a frequency distribution in

which the height of a bar corresponds to the observed which the height of a bar corresponds to the observed frequency of the category.frequency of the category.


EXHIBIT 20.EXHIBIT 20.11 Levels of Scale Measurement and Suggested Descriptive Levels of Scale Measurement and Suggested Descriptive StatisticsStatistics


Creating and Interpreting TabulationCreating and Interpreting Tabulation

• TabulationTabulation The orderly arrangement of data in a table or other The orderly arrangement of data in a table or other

summary format showing the number of responses to summary format showing the number of responses to each response category.each response category.

TallyingTallying is the term when the process is done by is the term when the process is done by hand. hand.

• Frequency TableFrequency Table A table showing the different ways respondents A table showing the different ways respondents

answered a question.answered a question. Sometimes called a Sometimes called a marginal tabulationmarginal tabulation..


Frequency Table ExampleFrequency Table Example


Cross-TabulationCross-Tabulation

• Cross-TabulationCross-Tabulation Addresses research questions involving relationships Addresses research questions involving relationships

among multiple less-than interval variables.among multiple less-than interval variables. Results in a combined frequency table displaying one Results in a combined frequency table displaying one

variable in rows and another variable in columns.variable in rows and another variable in columns.

• Contingency TableContingency Table A data matrix that displays the frequency of some A data matrix that displays the frequency of some

combination of responses to multiple variables.combination of responses to multiple variables.

• MarginalsMarginals Row and column totals in a contingency table, which Row and column totals in a contingency table, which

are shown in its margins.are shown in its margins.


EXHIBIT 20.EXHIBIT 20.22 Cross-Tabulation Tables from a Survey Regarding AIG and Cross-Tabulation Tables from a Survey Regarding AIG and Government BailoutsGovernment Bailouts


EXHIBIT 20.EXHIBIT 20.33 Different Ways of Depicting the Cross-Tabulation of Biological Sex and Different Ways of Depicting the Cross-Tabulation of Biological Sex and Target PatronageTarget Patronage


Cross-Tabulation (cont’d)Cross-Tabulation (cont’d)

• Percentage Cross-TabulationsPercentage Cross-Tabulations Statistical baseStatistical base – the number of respondents or – the number of respondents or

observations (in a row or column) used as a basis for observations (in a row or column) used as a basis for computing percentages.computing percentages.

• Elaboration and RefinementElaboration and Refinement Elaboration analysisElaboration analysis – an analysis of the basic – an analysis of the basic

cross-tabulation for each level of a variable not cross-tabulation for each level of a variable not previously considered, such as subgroups of the previously considered, such as subgroups of the sample.sample.

Moderator variableModerator variable – a third variable that changes – a third variable that changes the nature of a relationship between the original the nature of a relationship between the original independent and dependent variables.independent and dependent variables.


EXHIBIT 20.EXHIBIT 20.44 Cross-Tabulation of Marital Status, Sex, and Responses to the Cross-Tabulation of Marital Status, Sex, and Responses to the Question “Do You Shop at Target?”Question “Do You Shop at Target?”


Cross-Tabulation (cont’d)Cross-Tabulation (cont’d)

• How Many Cross-Tabulations?How Many Cross-Tabulations? Every possible response becomes a possible Every possible response becomes a possible

explanatory variable.explanatory variable. When hypotheses involve relationships among two When hypotheses involve relationships among two

categorical variables, cross-tabulations are the right categorical variables, cross-tabulations are the right tool for the job.tool for the job.

• Quadrant AnalysisQuadrant Analysis An extension of cross-tabulation in which responses An extension of cross-tabulation in which responses

to two rating-scale questions are plotted in four to two rating-scale questions are plotted in four quadrants of a two-dimensional table.quadrants of a two-dimensional table. Importance-performance analysisImportance-performance analysis


EXHIBIT 20.EXHIBIT 20.55 An Importance-Performance or Quadrant Analysis of HotelsAn Importance-Performance or Quadrant Analysis of Hotels


Data TransformationData Transformation

• Data TransformationData Transformation Process of changing the data from their original form Process of changing the data from their original form

to a format suitable for performing a data analysis to a format suitable for performing a data analysis addressing research objectives.addressing research objectives.

Bimodal


Problems with Data TransformationsProblems with Data Transformations

• Median SplitMedian Split

Dividing a data set into two categories by placing Dividing a data set into two categories by placing respondents below the median in one category and respondents below the median in one category and respondents above the median in another.respondents above the median in another.

The approach is best applied only when the data do The approach is best applied only when the data do indeed exhibit bimodal characteristics.indeed exhibit bimodal characteristics.

Inappropriate collapsing of continuous variables into Inappropriate collapsing of continuous variables into categorical variables ignores the information categorical variables ignores the information contained within the untransformed values.contained within the untransformed values.


EXHIBIT 20.EXHIBIT 20.66 Bimodal Distributions Are Consistent with Bimodal Distributions Are Consistent with Transformations into Categorical ValuesTransformations into Categorical Values


EXHIBIT 20.EXHIBIT 20.77 The Problem with Median Splits with Unimodal DataThe Problem with Median Splits with Unimodal Data


Index NumbersIndex Numbers

• Index NumbersIndex Numbers

Scores or observations recalibrated to indicate how Scores or observations recalibrated to indicate how they relate to a base number.they relate to a base number.

• Price indexesPrice indexes

Represent simple data transformations that allow Represent simple data transformations that allow researchers to track a variable’s value over time and researchers to track a variable’s value over time and compare a variable(s) with other variables.compare a variable(s) with other variables.

Recalibration allows scores or observations to be Recalibration allows scores or observations to be related to a certain base period or base number.related to a certain base period or base number.


EXHIBIT 20.EXHIBIT 20.88 Hours of Television Usage per WeekHours of Television Usage per Week


Calculating Rank OrderCalculating Rank Order

• Rank OrderRank Order Ranking data can be summarized by performing a Ranking data can be summarized by performing a

data transformation.data transformation. The transformation involves multiplying the frequency The transformation involves multiplying the frequency

by the ranking score for each choice resulting in a by the ranking score for each choice resulting in a new scale.new scale.


EXHIBIT 20.EXHIBIT 20.99 Executive Rankings of Potential Conference DestinationsExecutive Rankings of Potential Conference Destinations


EXHIBIT 20.EXHIBIT 20.1010 Frequencies of Conference Destination RankingsFrequencies of Conference Destination Rankings


EXHIBIT 20.EXHIBIT 20.1111 Pie Charts Work Well with Tabulations and Cross-TabulationsPie Charts Work Well with Tabulations and Cross-Tabulations


Computer Programs for AnalysisComputer Programs for Analysis

• Statistical PackagesStatistical Packages SpreadsheetsSpreadsheets

ExcelExcel

Statistical software:Statistical software: SASSAS SPSS (SPSS (Statistical Package Statistical Package

for Social Sciencesfor Social Sciences)) MINITABMINITAB


EXHIBIT 20.EXHIBIT 20.1212 SAS Computer Output of Descriptive StatisticsSAS Computer Output of Descriptive Statistics


EXHIBIT 20.EXHIBIT 20.1313 Examples SPSS Output for Cross-TabulationExamples SPSS Output for Cross-Tabulation


Computer Graphics and Computer Computer Graphics and Computer MappingMapping• Box and Whisker PlotsBox and Whisker Plots

Graphic representations of central tendencies, Graphic representations of central tendencies, percentiles, variabilities, and the shapes of frequency percentiles, variabilities, and the shapes of frequency distributions.distributions.

• Interquartile RangeInterquartile Range A measure of variability.A measure of variability.

• OutlierOutlier A value that lies outside the normal range of the data.A value that lies outside the normal range of the data.


EXHIBIT 20.EXHIBIT 20.1414 A 3-D A 3-D Graph Showing Fast-Food Consumption Patterns around the Graph Showing Fast-Food Consumption Patterns around the U.S.U.S.


EXHIBIT 20.15EXHIBIT 20.15 Computer Drawn Computer Drawn Box and Whisker Box and Whisker PlotPlot


InterpretationInterpretation

• InterpretationInterpretation

The process of drawing inferences from the analysis The process of drawing inferences from the analysis results.results.

Inferences drawn from interpretations lead to Inferences drawn from interpretations lead to managerial implications and decisions.managerial implications and decisions.

From a management perspective, the qualitative From a management perspective, the qualitative meaning of the data and their managerial implications meaning of the data and their managerial implications are an important aspect of the interpretation.are an important aspect of the interpretation.


CASE EXHIBIT 20.2CASE EXHIBIT 20.2––11 Shifts in Brand Choice Before and Shifts in Brand Choice Before and After Showing of Downy-Q Quilt After Showing of Downy-Q Quilt CommercialCommercial


CASE EXHIBIT 20.2CASE EXHIBIT 20.2–2–2 Pre/Post Increment in Choice of Downy-QPre/Post Increment in Choice of Downy-Q


CASE EXHIBIT 20.2CASE EXHIBIT 20.2–3–3 Adjective Checklist for Downy-Q Quilt CommercialAdjective Checklist for Downy-Q Quilt Commercial


CASE EXHIBIT 20.2CASE EXHIBIT 20.2–4–4 Product Attribute Checklist for Downy-QProduct Attribute Checklist for Downy-Q

chapter 20 basic data analysis: descriptive statistics © 2010 south-western/cengage learning. all...

Documents

basic crosstabulation

crosstabulation tables

crosstabulation contdhow

data analysis

marginal tabulation

frequency tablea table

data set

combined frequency table