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What is construction validity and what does it provide in constructs or operations


To provide complete and accurate view of research work, a given construct must draw confidence in its representation and ability to validate its own concepts based on its empirical findings (Straub, Boudreau & Gefen, 2004).

Content Validity

The construct would be more helpful and meaningful if the validities chosen reflect the heuristics presented in the study. Assessing content validity is very difficult as researchers are uncertain on how to rate the degree or level of completeness of their own research work. Content validity is basically sampling and evaluating the contents of research work for its validity (Straub, Boudreau & Gefen, 2004).

Construct Validity

Construct validity provides the measurement between constructs or operations. Construct validity appeals more to the content of the construct’s validity than the substance of the construct. It raises the fundamental question as to whether the construct fits in its application by the researcher in an attempt to capture the significance of the construct. The application of nomological network is applied to test if there are links similar to the ones existing on the construct literature. Construct validity focuses more on the likeness or differences of the construct linkages and their strengths on past literature (Straub, Boudreau & Gefen, 2004).

Predictive Validity

It is also known as concurrent validity and it establishes relationship between different measures and constructs through demonstration of posited measures directed on specific construct correlations. Predictive validity predicts outcomes for given variables but they also provide conceptual meaning applies to the constructs antecedents and ramifications. The goal of predictive validity is basically prediction while also reinforcing the concept of the theory base (Szajna, 1994).

The author has explained the differences as well as the similarities of the various validity measures. For example, the author has explained that predictive validity validates both the exogenous as well as the endogenous constructs based on the use of z-scores (Szajna, 1994).

The peer review research article by Klein and Olbrecht (2011) demonstrates the need to widen and work on more exploratory research work. The article summarizes the triangulation of qualitative and quantitative data analysis. The oral presentation and the other protocols provided as the basis of the research work requires more cognitive frameworks to present constructive and validated data. The intractable domains are also difficult to establish as the research relies on limited primary data. To assess the validity of the primary data I would require the questionnaires that were used to compile the research report (Straub, Boudreau & Gefen, 2004).

The non-theoretical work which bases its findings on the primary descriptive data would provide a basis for predictive validity. The predictive validity predicts outcomes for given variables but its conceptual meaning applies to the constructs antecedents and ramifications which are collaborated with the content validity. The research work can only be validated by confirming the constructs relationship with other research work from well established research streams. The article confirms that the validity of scientific that is based on positivist science needs based not only on highly biased observations but also on a series of random anecdotes that tests the intellectual constructs of the research presented. The article requires the rigor of intelligent, careful and thoughtful collection of primary data (Carrier et al, 1990).

The best description of my potential doctoral study is the exploratory that specifically probe the areas in HRM that are not well understood like valuation of human resources as a key element of a company’s capital structure.

Carrier, M. R., A. T. Dalessio, and S. H. Brown (1990). “Correspondence between Estimates of Content and Criterion-Related Validity Values,” Personnel Psychology (43) 1 (spring), pp. 85-100.

Klein, T. and Olbrecht, M. (2011) Triangulation of Qualitative and Quantitative Methods in Panel Peer Review Research, International Journal for Cross-Disciplinary Subjects in Education (IJCDSE), Volume 2, Issue 2, June, pg 345 -348.

Szajna, B. (1994) “Software Evaluation and Choice: Predictive Validation of the Technology Acceptance Instrument,” MIS Quarterly (17) 3, pp. 319-324. Straub, D.,  & Boudreau, M., & Gefen, D. (2004) Validation Guidelines for Positivist Research, Communications of the Association for Information Systems, Vol 13, pg 380 – 427

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