Indicator Reliability in WarpPLS Outputs
Johnny Amora
The following are part of the WarpPLS outputs: Combined loadings, Pattern loadings, Structure loadings, and Indicator Weights. Which one of such loadings is equivalent to the indicator loading described by Henseler, Ringle and Sinkovics (2009). On page 300 (Table 2) of their paper, it is indicated that indicator reliability for reflective measurement models, the absolute standardized outer loading should be higher than .7.
Philippines
Ned Kock
Hi Johnny.
The following selected text may be useful, starting from page 35 of the WarpPLS 3.0 User Manual:
Combined loadings and cross-loadings are provided in a table with each cell referring to an indicator-latent variable link (see Figure H.5). Latent variable names are listed at the top of each column, and indicator names at the beginning of each row. In this table, the loadings are from a structure matrix (i.e., unrotated), and the cross-loadings from a pattern matrix (i.e., rotated).
Since loadings are from a structure matrix, and unrotated, they are always within the -1 to 1 range. This obviates the need for a normalization procedure to avoid the presence of loadings whose absolute values are greater than 1. The expectation here is that loadings, which are shown within parentheses, will be high; and cross-loadings will be low.
For research reports, users will typically use the table of combined loadings and cross-loadings provided by this software when describing the convergent validity of their measurement instrument. A measurement instrument has good convergent validity if the question-statements (or other measures) associated with each latent variable are understood by the respondents in the same way as they were intended by the designers of the question-statements. In this respect, two criteria are recommended as the basis for concluding that a measurement model has acceptable convergent validity: that the P values associated with the loadings be lower than 0.05; and that the loadings be equal to or greater than 0.5 (Hair et al., 1987; 2009).
Indicators for which these criteria are not satisfied may be removed. This does not apply to formative latent variable indicators, which are assessed in part based on P values associated with indicator weights. If the offending indicators are part of a moderating effect, then you should consider removing the moderating effect if it does not meet the requirements for formative measurement. Moderating effect latent variable names are displayed on the table as product latent variables (e.g., Effi*Proc).
The following criteria, one more conservative and the other two more relaxed, are suggested in the assessment of the reliability of a measurement instrument. These criteria apply only to reflective latent variable indicators. Reliability is a measure of the quality of a measurement instrument; the instrument itself is typically a set of question-statements. A measurement instrument has good reliability if the question-statements (or other measures) associated with each latent variable are understood in the same way by different respondents.
More conservatively, both the compositive reliability and the Cronbach’s alpha coefficients should be equal to or greater than 0.7 (Fornell & Larcker, 1981; Nunnaly, 1978; Nunnally & Bernstein, 1994). The more relaxed version of this criterion, which is widely used, is that one of the two coefficients should be equal to or greater than 0.7. This typically applies to the composite reliability coefficient, which is usually the higher of the two (Fornell & Larcker, 1981). An even more relaxed version sets this threshold at 0.6 (Nunnally & Bernstein, 1994). If a latent variable does not satisfy any of these criteria, the reason will often be one or a few indicators that load weakly on the latent variable. These indicators should be considered for removal.
AVEs are normally used for discriminant validity assessment and, less commonly, for convergent validity assessment. For discriminant validity assessment, AVEs are used in conjunction with latent variable correlations in the assessment of a measurement instrument’s discriminant validity. This is discussed in more detail later, together with the discussion of the table of correlations among latent variables. For convergent validity assessment, the threshold frequently recommended for acceptable validity is 0.5 (Fornell & Larcker, 1981), and applies only to reflective latent variables.
Best, Ned
Johnny Amora
Philippines
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