Market ResearchContentsMissing Value analysis2Reliability testing2Multi Collinearity3Validity Check4Multiple Regression4Discriminant Analysis5Factor Analysis8Cluster Analysis10Multidimensional scaling14

Missing Value analysis

Done when few responses are not recorded due to some problem

Reliability testing

Based in Cronbach alpha >.6Item total statistics check cronbach alpha if item deleted(if in any variable it is greater than the existing value then delete the question to increase it)

Data reduction technique Cronbach alpha should be greater than .6Analysis > Scale > Reliability analysis > Statistics > Scale is item deleted (shows if the item is deleted by how much will the cronbach alpha will increase)To identify the excluded data first use frequency table and then missing data analysis

Reliability Statistics

Cronbach's AlphaN of Items

.8105

Item-Total Statistics

Scale Mean if Item DeletedScale Variance if Item DeletedCorrected Item-Total CorrelationCronbach's Alpha if Item Deleted

Rating of Quality of Mechandise at Sears16.9712.698.616.769

Rating of Variety and Assortment of Mechandise at Sears16.6012.157.622.766

Rating of Returns and Adjustment Policy of Sears16.3312.275.619.767

Rating of Service of Personnel of Sears17.3211.869.549.793

Rating of Perception of Fair Prices at Sears16.7012.938.598.774

Multi Collinearity

Analyze> regression > linearStatistics > Collinearity diagnosistics(Compare each variable with all other variables, dependent and independent combinations)

VIF (variable inflation factor) not ok (both questions are the same remove one of them)

Coefficientsa

ModelCollinearity Statistics

ToleranceVIF

1Rating of Variety and Assortment of Mechandise at Sears.6241.603

Rating of Returns and Adjustment Policy of Sears.5801.725

Rating of Service of Personnel of Sears.6801.471

Rating of Perception of Fair Prices at Sears.6791.472

Rating of Convenience of Location of Sears.8791.138

a. Dependent Variable: Rating of Quality of Mechandise at Sears

Validity Check1. Convergent- correlation of items of the same factor should be high2. Discriminant- correlation of items of different factors should be low

Multiple Regression

Model Summaryb

ModelRR SquareAdjusted R SquareStd. Error of the Estimate

1.927a.859.830.81681

a. Predictors: (Constant), attract, strong, shiny, fresh, decay

b. Dependent Variable: cavity

Model fit via R^2 >.6(how much of the dependent variable is explained by the independent variable) ANOVAa

ModelSum of SquaresdfMean SquareFSig.

1Regression97.854519.57129.334.000b

Residual16.01224.667

Total113.86729

a. Dependent Variable: cavity

b. Predictors: (Constant), attract, strong, shiny, fresh, decay

Accept or reject the null hypothesis( no relationship) Considering the significance valueCoefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig.

BStd. ErrorBeta

1(Constant)3.1361.2512.506.019

shiny.134.151.093.889.383

strong.550.129.5714.272.000

fresh.162.160.1131.016.320

decay-.456.134-.439-3.399.002

attract-.252.162-.177-1.558.132

a. Dependent Variable: cavity

Again consider the significance values to reject or accept the variablesIf greater than .05 reject

Equations based of B value

Discriminant Analysis

Case 1:

Y= a + b1x1 + b2x2 + b3x3 + b4x4 + b5x5Y loyalty (highly loyal, loyal, neutral , not loyal , highly disloyal)X1 ageX2 genderX3 freq.X4 incomeX5 heightAll independent variable should be metric for multiple discriminant analysisBy metric we mean which can be measured unlike gender

If non metric variable exist we use logistic regression

NM = M (multiple discriminant analysis)NM= M/NM (logistic regression)

Analyze > Classify > Discriminant (grouping variable / Define Range) Enter Independent data Statistics (Means , univariate ANNOVAs, Winthin-group correlation) Classify( summary table , leave one out classification, all groups equal, within-groups) Save ( predictive group membership)Wilks' Lambda

Test of Function(s)Wilks' LambdaChi-squaredfSig.

1.53316.6903.001

To test Model fit (Sig. scale> multidimensional scale (Alscale)Model> Euclidean distance, level of measurement (ordinal), conditionality (matrix), Dimension (min 1 max 3 depending upon need)Options> individual subject plots

Analyze STRESS FACTOR AND R^2

Stress factor.2 Poor fit.1 - Fair.05 - Good.025 - Excellent.000 Perfect

RSQ >.6

Dimension 1 and 2 to be given by the researcherNew entrant can position the brand in the top left corner