This article presents a framework for representing constructs and their indicators at various depths, aiming to guide researchers in choosing the appropriate level of specificity versus generality. The framework integrates construct validation methods based on confirmatory factor analysis of multitrait-multimethod (MTMM) matrices. The authors apply this framework to measure work values using the Work Aspect Preference Scale (WAPS). Results show that the WAPS performs better when used to represent relatively specific work values rather than more global, general values. Further analyses support the generalizability of the WAPS factor structure for both men and women, although gender differences were found on structured means for several latent value dimensions. The article discusses the advantages and disadvantages of different models (total disaggregation, partial disaggregation, partial aggregation, and total aggregation) and provides guidelines for selecting the appropriate model based on the research context and goals. The framework also addresses issues such as generalizability, structured means, and tests of convergent and discriminant validity.This article presents a framework for representing constructs and their indicators at various depths, aiming to guide researchers in choosing the appropriate level of specificity versus generality. The framework integrates construct validation methods based on confirmatory factor analysis of multitrait-multimethod (MTMM) matrices. The authors apply this framework to measure work values using the Work Aspect Preference Scale (WAPS). Results show that the WAPS performs better when used to represent relatively specific work values rather than more global, general values. Further analyses support the generalizability of the WAPS factor structure for both men and women, although gender differences were found on structured means for several latent value dimensions. The article discusses the advantages and disadvantages of different models (total disaggregation, partial disaggregation, partial aggregation, and total aggregation) and provides guidelines for selecting the appropriate model based on the research context and goals. The framework also addresses issues such as generalizability, structured means, and tests of convergent and discriminant validity.