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Table of ContentsQ1: Factor analysis:.................................................................................................................................3Q2: Regression analysis:..........................................................................................................................5Q3: Hypothesis that industry experience mediates the effect of cognitive ability on job performance..6Q4: Other factors, aside from statistical support in the regression model..............................................8Q5: Hypothesis that industry experience moderates the effect of cognitive ability on job performance.................................................................................................................................................................8REFERENCES................................................................................................................................................9APPENDIX..................................................................................................................................................10

Q1: Factor analysis:Interpretation of Outputs for Big five personality traits, Cognitive Analysis and Job PerformanceAnalysis:Big five personality traits:Correlation Matrix:A basic screening of this output tells us how many variables aresignificant. Significance of a variable is generally shown when the majority of the values equalor more than 0.05. After screening, correlation coefficients of value equal or more than 0.9 areobserved from these selected significant variables. Out of 25 personality traits, 2 personalitytraits viz. a1, o1 have 15 significant values over 0.05.Total Variance Explained:Eigenvalues are variances of principal components. The percentageof variance for these seven dimensions account for 54.75% of the total variance. The firstEigenvalue will always be the highest accounting for most variance (DeSantis And et. Al, 2014).Extraction Sums of Squared Loading reproduces exact values as generated by column 2 to 3,focusing only on components with Eigenvalue more than 1.Rotation Sums of Squared Loadingsrepresents varimax rotation that has redistributed total variance over 7 component making itmore concise and smooth.Scree Plot:Scree Plot is a graphical representation of Eigenvalues against their components.Eigenvalues have been plotted on the Y-axis and their related component numbers have beenplotted on the X-axis respectively. One can observe that after the 7thdimension in componentspace, the graph shows a flat line conveying that each successive component with varianceaccounted goes on decreasing.Component Matrix:The table shows a component matrix before rotation. It can be seen thatexcept for a2, o4, c5, c4,a1, a3, c2 and a4,most of the personality traits are loaded on Factor 1.On the other hand, apart from n5, c5, c4 and c2, none of the personality traits are loaded onFactor 3.Rotated Component Matrix:Rotated Component Matrix forms one of the key outputs ofPrincipal Components Analysis in SPSS. The Output shows that out of all the components,Component 1 has the highest correlation with n2 (+0.752) and lowest correlation with n3 (-

0.755). Component 5 has highest correlation with c5 (+0.838) trait out of c5 and c4 personalitytraits(Triola, 2013).Component Transformation Matrix:This matrix displays the correlation among componentsthat exists before and after the rotation. It has used Varimax as a rotational method with KaiserNormalization.Cognitive analysis:Correlation Matrix:In this output significance of a variable is generally shown when themajority of the values equal or more than 0.05. Cognitive Variable x1 has highest correlationwith x8 variable (+0.270) and least correlation with x9 (+0.072).Communalities:Communalities represent the ratio of variance accounted for in every variableby extracted factors. Over 58.8% of the variance has been accounted for in x2 and only 21.4% ofthe variance has been accounted for in x11.Total Variance Explained:Eigenvalues are variances of principal components. The percentageof variance for these three dimensions account for 46.59% of the total variance. Extraction Sumsof Squared Loading reproduces exact values as generated by column 2 to 3, focusing only oncomponents with Eigenvalue more than 1. Varimax rotation has redistributed total variance over3 dimensions in component space making it more concise and smooth.Scree Plot:In the aboveScree Plot, Eigenvalues have been plotted on the Y-axis and theirrelated component numbers have been plotted on the X-axis respectively. One can observe thatafter the 3rddimension in component number, a flat line is formed(DeSantis, Bryan and Jemal,2014).Component Matrix:The table shows a component matrix before rotation. It can be seen thatmost of the cognitive variables are loaded on Factor 1. On the other hand, apart from x1, x2 andx10, none of the cognitive variables are loaded on Factor 3.Rotated Component Matrix:The generated output shows that out of all the components,Component 1 has the highest correlation with x10 (+0.688) and lowest correlation with x12(+0.500). Component 3 has highest correlation with x2 cognitive variable (+0.761) and lowestcorrelation with x11 cognitive variable (+0.327).

Component Transformation Matrix:This matrix displays the correlation among componentsthat exists before and after the rotation. It has used Varimax as a rotational method with KaiserNormalization.Job performance analysis:Correlation Matrix:A basic screening of this output tells us how many variables are significant.Significance of a variable is generally shown when the majority of the values equal or more than0.05. R1 includes highest correlation with R8 (+0.689) and least correlation with R10 (+0.111).Communalities:Communalities generated in the above output shows over 79.5% of thevariance accounted for in R8 and only 51.4% of the variance has been accounted for in R10.Total Variance Explained:The above output indicates the percentage of variance for twocomponents account for 65.77% of the total variance(Blackwell, Lucas and Clarke, 2014).Extraction Sums of Squared Loading reproduces exact values focusing only on components withEigenvalue more than 1. Varimax rotation has redistributed total variance over 3 dimensions incomponent space making it more concise and smooth.Scree Plot:TheScree Plot has Eigenvalues plotted on the Y-axis and their related componentnumbers have been plotted on the X-axis respectively. One can observe that after the 2nddimension in component number, a flat line is formed.Component Matrix:The table shows a component matrix with most of the variables loaded onFactor 1. On the other hand, apart from R5, R2 and R10, none of the variables are loaded onFactor 2.Rotated Component Matrix:The table shows a component matrix before rotation. It can beseen that most of the cognitive variables are loaded on Factor 1. On the other hand, apart fromx1, x2 and x10, none of the cognitive variables are loaded on Factor 3.Component Transformation Matrix:This matrix displays the correlation among componentsthat exists before and after the rotation. It has used Varimax as a rotational method with KaiserNormalization.Q2: Regression analysis:ANOVA Interpretation:

Personality traits:The value of 0.078 indicates that p-value is greater than 0.05 showingsignificant difference between means.Cognitive Ability:The value of 0.499 indicates that p-value is greater than 0.05 showingsignificant difference between means.Experience:The value of 0.446 indicates that p-value is greater than 0.05 showing significantdifference between means(Searle and Khuri, 2017).Q3: Hypothesis that industry experience mediates the effect of cognitive ability on jobperformance

Q4: Other factors, aside from statistical support in the regression modelOne way ANOVA test calculates significant values for factors, apart from statisticalsupport in the regression model that are needed to make a compelling case for mediation. Thefactors having p-values more than 0.05 we cannot conclude significant difference exists. Thefactors with p-value less than 0.05 rejects null hypothesis taken for the research.Q5: Hypothesis that industry experience moderates the effect of cognitive ability on jobperformanceUnder question 3, it has been derived that the research undertaken is taken on thebasis of correct assumptions rejecting the null hypothesis and accepting the hypothesis taken bythe research to collect the data.

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