Rigor and Validity in Quantitative Research: Understanding the Essentials

In quantitative research, rigor and validity are two essential elements that play a crucial role in judging the strength of an experimental design. The concept of validity can usually be divided into two main categories: population validity and ecological validity.

A recurrent trend in higher education research has been to identify additional predictors of college success beyond traditional measures such as high school grade point average (HSGPA) and standardized test scores, given that a large percentage of unaccounted variance in college performance remains. To address this issue, researchers utilize three approaches to internal validity: content validity, criterion-related validity, and construct validity.

Internal validity is an estimate of the degree to which conclusions about causal relationships can be made based on the research design. In contrast, external validity refers to the extent to which the results of a study can be generalized to other populations, settings, or situations, commonly applied to laboratory research studies.

In essence, validity is defined as the extent to which a measure or concept is accurately measured in a study. This is achieved through measurement of reliability and validity, which are two closely related concepts that help demonstrate the rigour and usefulness of the researcher's work.

Reliability refers to the consistency of measurements or the degree to which an instrument measures the same with every use under the exact same conditions. In quantitative studies, reliability is usually estimated using internal consistency - the relationship/correlation between different results of a test or instrument.

Cronbach's α coefficient is a statistical test that 'splits' all the results in half and calculates the correlations between the two sets. This generates a single value between 0-1, where the closer the coefficient generated is to 1, the higher the reliability estimate of your instrument/test.

Rigor, in quantitative studies, refers to the extent researchers work to enhance the quality of the study. These procedures help demonstrate the rigour and usefulness of the researcher's work. As a way of measuring the likelihood of the researcher's result being misleading, statisticians developed procedures for expressing the likelihoods and accuracy of the results.

In conclusion, quantitative research is the process of a systematic investigation, primarily using numerical techniques (statistical, mathematical or computational), to test hypothetical generalisations. Understanding how validity and reliability are achieved in quantitative research is crucial for researchers seeking to conduct high-quality studies that produce meaningful results.

By emphasizing the importance of rigor and validity, researchers can ensure that their findings are reliable, accurate, and applicable to real-world situations. Ultimately, this enables them to make informed decisions and contribute to the advancement of knowledge in their field.

Rigor and validity in quantitative research