CHAPTER 8 Basic Data Analysis for Quantitative Research

ESSENTIALSESSENTIALS OF MARKETING RESEARCHOF MARKETING RESEARCHHair/Wolfinbarger/Ortinau/BushHair/Wolfinbarger/Ortinau/Bush

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Statistical Analysis

Every set of data collected needs some summary information developed that describes the numbers it containsCentral tendency and dispersion, Relationships of the sample data, andHypothesis testing

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ModeModeResponse Most

Often Givento a Question

ModeModeResponse Most

Often Givento a Question

MedianMedianMiddle Value

of a Rank OrderedDistribution

MedianMedianMiddle Value

of a Rank OrderedDistribution

Measures of Central Tendency

MeanMeanArithmetic Average

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Measures of Central Tendency

Each measure of central tendency describes a distribution in its own manner:for nominal data, the mode is the best

measure.for ordinal data, the median is generally the

best.for interval or ratio data, the mean is

generally used.

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Describes how close to the mean or other measure of central tendency, the rest of the values fall

Describes how close to the mean or other measure of central tendency, the rest of the values fall

Measures of Dispersions

RangeDistance between the smallest and largest

value in a set

Standard DeviationMeasure of the average dispersion of the values

about the mean

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Exhibit 12.3 Output for Measures of Dispersion

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Univariate Tests of Significance

Tests of one variable at a timez-testt-test

Appropriate for interval or ratio data

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Exhibit 12.4 Univariate Hypothesis Test Using X16

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Bivariate Statistical Tests

Compare characteristics of two groups or two variablesCross-tabulation with Chi-Square t-test to compare two meansAnalysis of variance (ANOVA) to compare

three or more means

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Exhibit 12.5 Cross-Tabulation

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Chi-Square Analysis

Chi-square analysis enables the Researcher to test for statistical

significance between the frequency distributions of two or more nominallyscaled variables in a cross-tabulation

table to determineif there is any association

between the variables

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Exhibit 12.6 SPSS Chi-Square Crosstab Example

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Comparing means

Requires interval or ratio dataThe t-test is the difference between the

means divided by the variability of random means

The t-value is a ratio of the difference between the two sample means and the std error

The t-test tries to determine if the difference between the two sample means occurred by chance

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Exhibit 12.7 Comparing Two Means with Independent Samples t-Test

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Exhibit 12.8 Paired Samples t-Test

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Analysis of Variance

Analysis of Variance (ANOVA) is a statistical technique that determines if three or more means are statistically different from each other

The dependent variable must be measurable; either interval or ratio scaled

The independent variable must be categorical

“One-way ANOVA” means that there is only one independent variable

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F-Test

The F-test is the test usedto statistically evaluate the differencesbetween the group means in ANOVA

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Follow-up Tests

Anova does not tell us where the significant differences lie – just that a difference exists TukeyDuncanScheffe

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Exhibit 12.9 Example ANOVA