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Canonical Analysis

• Introduction• Assumptions• Model representation• An output example• Conditions• Procedural steps• Mechanical steps

- with the use of attached file

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• The computer output can be obtained by opening a syntax window and typing SPSS commands. Click on File, click on New and then click on SPSS Syntax, and type the statements. The following box is the statements of the example in Section 7.0.

MANOVA EFFICACY USAGE WITH SUPPORT EXP/DISCRIM RAW STAN ESTIM CORR ALPHA (1)/PRINT SIGNIF(MULT UNIV EIGN DIMENR )/NOPRINTPARAM(ESTIM)/METHOD=UNIQUE/DESIGN .•

• The expression ‘MANOVA’ in statement 1 requests a multivariate analysis of variance. The dependent variables are computer self-efficacy and microcomputer usage. The absence of the word ‘BY’ indicates that there are no factors with lower measurement level present. The word ‘WITH’ indicates that the covariates with an interval measurement level will follow; these are management support and computer experience.

•• In the second statement, a canonical

correlation analysis is requested with ‘DISCRIM’. ‘STAN’ means that we would like to have the standardized canonical weights. With ‘CORR’ we request the correlations between the original variables and the canonical variables; these are the structure correlations. ‘ALPHA’ means that we establish the significance level of the canonical variables. The default for this is 0.15. By setting the maximum at 1 (‘ALPHA (1)’), we can make sure that all of the canonical variables, even the insignificant ones, will be included in the analysis.

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Introduction

• is a multivariate statistical model which facilitates the study of interrelationships among sets of multiple criterion (dependent) variables and multiple predictor (independent) variables

• is a powerful multivariate technique because it can address a wide range of objectives.

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Assumptions• correlation is used only when

there are a set of multiple dependent and independent variables

• conceptually linked sets of the variables before applying canonical correlation analysis

• performed on correlation or variance-covariance matrices that reflect only linear relationships

• all variables and all linear combinations of variables are normally distributed

• multicollinearity among either variable set will isolate the impact of any single variable and this makes interpretation less reliable

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Procedural Steps

1. classify two sets of variables or determine the magnitude of the relationships which may exist among the two sets

2. derives a set of weights for each set of dependent and independent variables such that the linear combinations themselves are maximally correlated

3. explain the nature of whatever relationships exist among the sets of dependent and independent variables, generally by measuring the relative contributions of each variable to the canonical functions (relationships) that are extracted

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Data Analysis

References

Min canonical loading to be used for the analysis

Min R2 values in the result

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Model Representation

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Mechanical Steps

1: Objectives of Canonical Correlation Analysis

2: Designing a Canonical Correlation Analysis

3:Testing the Assumptions

4: Deriving the Canonical Functions and Assessing Overall Fit

5: Interpreting the Canonical Variates

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Interpretation of results

• Refer to the paper for discussion starting from 122-123

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Assessing Overall Fit

• Refer to p122

Valid one

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Canonical Functions

• Refer to page 122

Here, it refers to only two significant functions are valid

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Testing the Assumptions

• Refer to page 121

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Designing a Canonical Correlation Analysis• Define:

– What is individual variables from ethics literature– What is seven ethical issues from ethics literature

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Objectives of Canonical Correlation Analysis

• Refer to the page 117 of the attached file:

• The objective is as follows: