1_ Factor Analysis

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Problem 1: Factor Analysis
Problem 1.1 Modularity Level of NPD Project
In Problem 1.1, we have beliefs about the constructs underlying the Modularity Level questions;
we believe that there are two constructs: Modular Design, and Modular Level.
Modularity Level (ml)
Modular Design
Modular Level
Social Capital (sc)
Social Interaction
Network Position
Project Leadership Style (ls)
Participating Style
Selling Style
Telling Style
Delegating Style
Team Member Diversification (in)
Knowledge Diversification
Social Category Diversification
NPD Performance (npd)
Product Prototype
Development Proficiency
Product Launch Proficiency Technological Core
Competency
Market Forecast Accuracy
Design Change Frequency
Product Development
Cycle Time
Innovation Level

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Analyze => Dimension Reduction => Factor
Next, select the variables ml1 through ml8.
Now click on Descriptives
Then click on the following: Initial solution (under Statistics), KMO and Barletts test of
sphericity (under Correlation Matrix)
Click on Continue

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Next, click on Extraction
Select Principal components from the Methods pulldown Click on Unrotated factor solution (under Display). Also, check the Scree plot box
(under Display)
Click on Continue
Now click on Rotation
Click on Varimax, then make sure Rotated solution is also checked.
Click on Continue

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Next, click on Options
Click on Sorted by size Click on Continue then OK
Output 1.1: Factor Analysis for Modularity Level
FACTOR
/VARIABLES ml1 ml2 ml3 ml4 ml5 ml6 ml7 ml8
/MISSING LISTWISE
/ANALYSIS ml1 ml2 ml3 ml4 ml5 ml6 ml7 ml8
/PRINT INITIAL KMO EXTRACTION ROTATION
/FORMAT SORT
/PLOT EIGEN
/CRITERIA MINEIGEN(1) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/SAVE BART(ALL)
/METHOD=CORRELATION.

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Testsofassumptions.
Shouldbegreaterthan.50indicatingsufficientitemsforeachfactor.Shouldbesignificant(lessthan.05),indicatingthatthecorrelationmatrixissignificantlyfromanidentitymatrix,inwhichcorrelationbetweenvariablesareallzero.
Thesecommunalitiesrepresenttherelationbetweenthevariableandallothervariables(i.e.,thesquaredmultiplecorrelationbetweentheitemandallotheritems).Shouldbebiggerthan.50tobeusedasareference,notasadeletecriteria.
Eigenvaluesrefertothevarianceexplainedoraccountedfor.Percentofvarianceforeachcomponentbeforeandafterrotation.

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The items cluster into these two groups
defined by high loadings.
Thescreeplotshowsthatafterthefirstfivecomponents,increasesintheeigenvaluesdecline,andtheyarelessthan1.0.

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Interpretation of Output 1.1
The factor analysis program generates a number of tables depending on which options have
chosen. The first table in Output 1.1 is the KaiserMeyerOlkin (KMO) measure which tells one
whether or not enough items are predicted by each factor. The KMO test should be greater
than .50. The Barlett test should be significant (i.e., a significance value of less than .05); this
means that the variables are correlated highly enough to provide a reasonable basis for factor
analysis.
Next, the Total Variance Explained table shows how the variance is divided among the 8
possible factors. Note that two factors have eigenvalues (a measure of explained variance)
greater than 1.0, which is a common criterion for a factor to be useful. When the eigenvalue is less
than 1.0, this means that the factor explains less information than a single item would have
explained. The computer has looked for the best twofactor solution by rotating two factors.
For this and all analyses, we will use an orthogonalrotation (varimax). This means that the final
factors will be as uncorrelated as possible with each other. As a result, we can assume that the
information explained by one factor is independent of the information in the other factors. Werotate the factors so that they are easier to interpret. Rotation makes it so that, as much as
possible, different items are explained or predicted by different underlying factors, and each factor
explains more than one item. One thing to look for in the Rotated Matrix of factor loadings is the
extent to which simple structure is achieved.
The Rotated Factor Matrix table, which contains these loadings, is key for understanding the
results of the analysis. Note that the computer has sorted the 8 modularity level of NPD project
(ml1 to ml8) into two overlapping groups of items, each which has a loading of . 60 or higher . 60
means the absolute value, or value without considering the sign, is greater than .60). Actually,
every item has some loading from every factor, but there are blanks in the matrix where weightswere less than . 60. Within each factor (to the extent possible), the items are sorted from the one
with the highest factor weight or loading for that factor (i.e., ml4for factor 1, with a loading of .836)
to the one with the lowest loading on that first factor (ml5). Loadings resulting from an orthogonal
rotation are correlation coefficients of each item with the factor, so they range from 1.0 through 0
to +1.0. Usually, factor loadings lower than . 60 are considered low, which is why we suppressed
loadings less than . 60.
Principal axis factor analysis with varimax rotation was conducted to assess the underlying
structure for the eight items of the Modularity Level Questionnaire. Two factors were requested,
based on the fact that the items were designed to index two constructs: modular design, and
modular level. After the rotation, the first factor accounted for 37.5% of the variance, and the
second factor accounted for 65.8%. Table 1.1 display the items and factor loadings for the rotated
factors, with loadings less than .60 omitted to improve clarity.

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Table 1.1Factor Loadings for the Rotated Factors Modularity Level
ItemsFactor Loading
Communality1 2
ml4 Our NPD project use modularized design 0.821 0.681
ml1 Our NPD project share common modules 0.734 0.616
ml2 Our new product features are designed around a standardbase unit 0.732 0.637
ml3 Our new products can be customized by adding featuremodules as requested
0.836 0.698
ml5 Our new product feature modules can be added to astandard base unit
0.677 0.545
ml6 Our new product modules can be rearranged by endusersto suit their needs
0.837 0.727
ml7 Our new product could partially upgrade, conveniently wearand tear or adapt new components
0.830 0.706
ml8 Our new product modules can be reassembled into
different forms
0.781 0.661
Eigenvalues 3.001 2.270% of variance 37.510 65.889
The first factor, which seems to index competence, loads most strongly on the first five items, with
loadings in the first column. The second factor was, which also seems to index competence
composed of the three items with loadings in column 2 of the table.

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Problem 1.2 Leadership Style of NPD Project
In Problem 1.2, we have beliefs about the constructs underlying the Leadership Style questions;
we believe that there are four constructs: Participating Style, Selling Style, Telling Styleand
Delegating Styles.
Modularity Level (ml)
Modular Design
Modular Level
Social Capital (sc)
Social Interaction
Network Position
Project Leadership Style (ls)
Participating Style
Selling Style
Telling Style
Delegating Style
Team Member Diversification (in)
Knowledge Diversification
Social Category Diversification
NPD Performance (npd)
Product Prototype
Development Proficiency
Product Launch Proficiency
Technological CoreCompetency
Market Forecast Accuracy
Design Change Frequency
Product Development
Cycle Time
Innovation Level

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Analyze => Dimension Reduction => Factor
Next, select the variables ls1 through ls16.

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Now click on Descriptives
Then click on the following: Initial solution (under Statistics), KMO and Barletts test of
sphericity (under Correlation Matrix)
Click on Continue
Next, click on Extraction
Select Principal components from the Methods pulldown
Click on Unrotated factor solution (under Display). Also, check the Scree plot box
(under Display)
Click on Continue
Now click on Rotation Click on Varimax, then make sure Rotated solution is also checked.
Click on Continue

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Next, click on Options
Click on Sorted by size Click on Continue then OK
Output 1.2: Factor Analysis for Leadership Style
FACTOR
/VARIABLES ls1 ls2 ls3 ls4 ls5 ls6 ls7 ls8 ls9 ls10 ls11 ls12 ls13 ls14 ls15 ls16
/MISSING LISTWISE
/ANALYSIS ls1 ls2 ls3 ls4 ls5 ls6 ls7 ls8 ls9 ls10 ls11 ls12 ls13 ls14 ls15 ls16
/PRINT INITIAL KMO EXTRACTION ROTATION
/FORMAT SORT
/PLOT EIGEN
/CRITERIA MINEIGEN(1) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/SAVE BART(ALL)
/METHOD=CORRELATION.

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Testsofassumptions.
Shouldbegreaterthan.50indicatingsufficientitemsforeachfactor.
Shouldbesignificant(lessthan.05),indicatingthatthecorrelationmatrixissignificantlyfromanidentitymatrix,inwhichcorrelationbetweenvariablesareallzero.
Thesecommunalitiesrepresenttherelationbetweenthevariableandallothervariables(i.e.,thesquaredmultiplecorrelationbetweentheitemandallotheritems).Shouldbebiggerthan.50tobeusedasareference,notasadeletecriteria.

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Deleted ls10

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Deleted ls4
Deleted ls8

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Deleted ls16
Deleted ls11

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Deleted ls14
Deleted ls15