Assessing psychometric properties of scales: a case study

Abstract

The substantially different factor structure identified suggests that this scale requires further refinement and testing. This case study highlights the importance of a systematic and comprehensive approach to determining construct validity of scales, thus enabling researchers to determine their suitability as data collection instruments.

Clinical Learning Environment Inventory: factor analysis

“…In the present paper, we report a principal components analysis (PCA) of the CLEI, to compare any underlying factor structure with the original scales of the CLEI prior to using the tool to evaluate the attributes of the clinical learning models. This strategy has recently been recommended for scales with predetermined dimensions (Marshall et al 2007). We believe that this is the first time it has been used to validate the CLEI's original structure.…”

“…One aspect of the project was to determine what impact, if any, the clinical placement model had on students' perceptions of their clinical learning environments. The Clinical Learning Environment Inventory (CLEI) (Chan 2001(Chan , 2003 was selected for this purpose because the items reflected dimensions of clinical settings that seemed to be important from our own research on clinical learning in a wide variety of settings (Jolly et al 1996, Murray et al 1997, Marshall et al 2007). Furthermore, other researchers had also used the CLEI, which was developed in Australia (Henderson et al 2006b, Darcy Associates 2009, and the items seemed wellmatched to the environments we were investigating, had a high degree of face validity and the resulting factors seemed plausible.…”

Exploring Determinants to Explain Aspects of Individual Investors’ Financial Behaviour

“…Exploratory analysis was carried out using SPSS version 18. The KMO value of the data was found to be 0.79, which is acceptable when above 0.50 (Marshall et al, 2007). The Bartlett's Test of Sphericity was also significant (x2 = 727.972; df =153; p=.000<.05).…”

“…The anti-image matrix was generated, and removed all items that were below 0.5 (Marshall et al, 2007). This was followed by using different rotational methods.…”

Exploring the Key Success Factors of Mobile Commerce in Taiwan

“…The measure of the KMO sampling adequacy is 0.956; this is greater than 0.7, which implies that factor analysis is appropriate for these data. Several scholars consider a sample is acceptable for factor analysis with KMO value above 0.5 [58]. Table 2 indicates that the result of Bartlett's test of sphericity is highly significant as it is less than 0.05 (p = 0.000); Therefore, factor analysis is suitable for this study.…”