The Cooper-Norcross Inventory of Preferences: Measurement invariance across international datasets and languages

ABSTRACT

Objective

The Cooper-Norcross Inventory of Preferences (C-NIP) is a brief, multidimensional measure of clients’ therapy preferences. This study aimed to examine the factor structure and measurement invariance of the C-NIP.

Method

Fifteen datasets (N = 10,088 observations) representing the C-NIP in nine language versions were obtained from authors of psychometric studies. Confirmatory factor analysis and exploratory structural equation modeling were used to analyze the data.

Results

None of the proposed models adequately fit the data. Therefore, a new model was developed that sufficiently fit most of the C-NIP version 1.1 datasets. The new model was invariant up to the strict and means levels across genders, ages, and psychotherapy experience but only up to the metric level across translations.

Conclusions

The C-NIP can be used to compare men and women, people of diverse ages, and people with some vs. no experience with psychotherapy. Lower reliabilities of the C-NIP scales are a limitation.

Key words:

• Cooper-Norcross Inventory of Preferences
• C-NIP
• psychotherapy
• preferences
• measurement invariance

Acknowledgement

We thank Agostino Brugnera, Peter E. Heinze, Micaela S. Malosso, Ömer Özer, Pablo Santangelo, and Aurélie Volders for sharing their data.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure Statement

Mick Cooper and John C. Norcross co-developed the Cooper–Norcross Inventory of Preferences (C-NIP) and receive a licensing fee for its commercial use. The C-NIP is in the public domain for individual users under the license CC BY-NC-ND 4.0.

Preregistration

This study was preregistered; see https://doi.org/10.17605/OSF.IO/M35GD.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10503307.2023.2255371.

Notes

1 Chen (2007) used an SRMR definition based on the covariance matrix only, which is insensitive to constraining intercepts. As a result, they suggested a lower cutoff of 0.010 for scalar invariance and N > 300. However, the more common SRMR index covers all the residuals of covariance (lower triangle, including diagonal) and intercept matrices. For detailed information, see Asparouhov and Muthén (2018), who called Chen’s (2007) index “EFA SRMR” and the later definition “CFA SRMR”. This kind of SRMR index is primarily implemented in the lavaan package (Rosseel, 2012), and as it includes residuals of intercepts, it is more sensitive to their constraining in the scalar invariance step. Hence, we used a less stringent criterion for this step.

Additional information

Funding

This study was supported by the Czech Ministry of Education, Youth, and Sports under Grant MUNI/A/1446/2022.