Proposing an Achievement Simulation Methodology to Allow the Estimation of Individual in Clinical Testing Context

Abstract

Information from the population should be directly transposed to the individual level only under strict conditions of stationarity and homogeneity. In general, psychological phenomena are neither stationary nor homogeneous. Furthermore, the individual parameters must be estimated. The usual techniques of estimating the individual are rarely attainable. This occurs because in order to get valid estimates of individual parameters, many occasions of the same test must be performed. In this article, we propose an approach (simerg algorithm) to estimate the individual, so that a proper clinical testing for the individual in the context of testing setting that require the respondents' performance becomes viable. We present the rationale of simerg and apply it to estimate the verbal comprehension ability of three individuals, which performed, in 90 different occasions, the 60 items of this ability. In this application, we show how simerg uses the initial empirical performance of these persons and simulates their whole performance. We evaluate the validity of simerg using different measures to predict the empirical performance. We conclude that simerg produces valid estimates. Further studies in a variety of populations and tests that require respondents' performance are needed to enable a stronger conclusion about the validity of simerg.

Measuring the structural validity of two Nordoff-Robbins scales for a patient with Autism

“…There is an extensive body of arguments sustaining the postulate that the current clinic practice is not adequate. The interested reader can read more details in the works of Jelihovschi and Gomes (2019), Gomes, Araujo, Nascimento and Jelihovschi (2018), Ferreira and Gomes (2017), Gomes and Golino (2015), as Gomes, Araujo, Ferreira, and Golino (2014). To sum up, the measurement of the individual needs to be estimated if the clinician wants to produce proper inference about this person.…”

The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets

“…Educational large-scale assessments, such as the International Association for the Evaluation of Educational Achievement (IEA) (Härnqvist, 1975), the Programme for International Student Assessment (PISA) (OECD, 2019), and the National Exam of Upper Secondary Education (ENEM) (Brasil/INEP, 2015) are, essentially, complex datasets. Some properties that constitute these assessments as complex datasets are the following: (1) They have large amounts of information about students and their socioeconomic, psychological, familiar, and educational backgrounds; (2) not by chance, they involve many non-linear relationships among the variables; (3) besides, they have a large set of nominal variables with many categories.…”

Angioedema Causado Por Medicamentos Inibidores Da Enzima Conversora De Angiotensina

“…This practice is sustained by the assumption that it is technically correct to directly transpose information based on population to each individual from this population. This direct transposition is technically wrong, according the ergodic theorems, and the interested reader can read the works of Jelihovschi and Gomes (2019), Gomes, Araujo, Nascimento andJelihovschi (2018), Ferreira and, Gomes and Golino (2015), Gomes, Golino and Costa (2013), as well Gomes, Araujo, Ferreira, and Golino (2014).…”