Findings suggest that for women with fibromyalgia who experience their emotions intensely, an emotional disclosure or expression intervention may be beneficial. This hypothesis requires verification in experimental studies.

Prediction accuracy of academic achievement for admission purposes requires adequate sensitivity and specificity of admission tools, yet the available information on the validity and predictive power of admission tools is largely based on studies using correlational and regression statistics. The goal of this study was to explore signal detection theory as a tool to extend the available information; signal detection theory allows for comparisons of selection outcomes on both group and individual levels and the development of tailor-made criteria for specific programmes and admission goals. We investigated who would or would not have been admitted applying specific criteria for each of three common admission tools, how many admitted students would fail and how many applicants who would have been successful would be rejected. Both comparisons at an individual level and the receiver operating characteristic curves at a group level revealed that scores obtained in a programme-specific matching programme and non-cognitive factors appear more valuable than regression statistics suggest when it comes to admitting applicants who will become successful students. Signal detection theory allows not only for admission-goal-specific and programme-specific fine-tuning of the content of admission tools, it also informs about the effects of criteria and thus allows the setting of criteria.

Using multiple admission tools in university admission procedures is common practice. This is particularly useful if different admission tools uniquely select different subgroups of students who will be successful in university programs. A signal-detection approach was used to investigate the accuracy of Secondary School grade point average (SSGPA), an admission test score (ACS), and a non-cognitive score (NCS) in uniquely selecting successful students. This was done for three consecutive first year cohorts of a broad psychology program. Each applicant's score on SSGPA, ACS, or NCS alone-and on seven combinations of these scores, all considered separate "admission tools"-was compared at two different (medium and high) cut-off scores (criterion levels). Each of the tools selected successful students who were not selected by any of the other tools. Both sensitivity and specificity were enhanced by implementing multiple tools. The signal-detection approach distinctively provided useful information for decisions on admission instruments and cut-off scores.

Signal Detection Theory (SDT) is rarely used in higher education, yet has much potential in informing decision-making. In this methodological paper, we describe the potential of SDT for different higher education contexts and demonstrate its practical application. Both the commonly used regression analyses and SDT analyses provide information on the accuracy of a predictor, and thus which instrument(s) to use. SDT analyses, in addition, provide information on the effects of setting specific cut-off scores on outcomes of interest. SDT provides the sensitivity and specificity information for the chosen instrument(s) at specific cut-off scores (criteria in SDT). This allows for evidence-informed, deliberate choice of cut-off scores to steer toward desired outcomes. Depending on how undesirable false positives and false negatives are considered in a specific situation, a lower or higher cut-off score can be deemed adequate. Using SDT analyses in our example, we demonstrate how to use the results to optimize “real-life” student selection. However, selection is only one of many decision-making practices where SDT is applicable and valuable. We outline some of the areas within higher education decision-making and quality assurance, where SDT can be applied to answer specific questions and optimize decision-making.

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