The measurement of reliable and valid indicators of early child development is necessary for assessing phenomena and is useful in the monitoring of ongoing efforts to eradicate inequalities in the social determinants of health. There is an increasing awareness of the contextual, cultural, and developmental influences on constructs used in early child development (ECD) research. Using a measurement perspective, this dissertation examined the issue of measurement invariance and psychometric validity in early child development research. A construct violates the principle of invariance when two persons from different populations who are theoretically identical on the construct being measured have different scores on it. This dissertation consists of three journal-style manuscripts (published or under review) that were used as examples to address the importance of the issue of measurement invariance and psychometric validity in ECD research using data from two unique areas: autism and executive functioning. The three data sets were collected on pre-school children with parents and or teachers as informants and were chosen to represent different levels of data collection – clinical, community, and population. These data sets allowed for the examination of measurement invariance by type of informant, sex, and age of child. The results from the three studies illustrate the importance of assessing measurement invariance in ECD and whether or not the instruments examined can be used to assess sub-group differences with confidence. A lack of measurement invariance found for two of the studies, suggests that observed group differences in latent constructs could be attributed, in part, to measurement bias. More importantly, bias in the measurement of the constructs of severity of social impairment symptoms in autism, and executive functioning across groups could have an impact on services such as patient treatment. These biases could also influence public policy development, particularly when there may be an underlying need for a cross-group approach where belief systems may affect the meaning and structure of constructs. In summary, measurement invariance should be a prerequisite for making any meaningful comparisons across groups. A requirement of establishing measurement invariance should be included in the guidelines for comparative research studies as a necessary first step before an instrument is adopted for use.

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Les maladies neurodégénératives, telles que la maladie d'Alzheimer (AD) et Charcot Marie Tooth (CMT), sont des maladies complexes. Leurs mécanismes pathologiques ne sont toujours pas bien compris et les progrès dans la recherche et le développement de nouvelles thérapies potentielles modifiant la maladie sont lents. Les données catégorielles, comme les échelles de notation et les données sur les études d'association génomique (GWAS), sont largement utilisées dans les maladies neurodégénératives dans le diagnostic, la prédiction et le suivi de la progression. Il est important de comprendre et d'interpréter ces données correctement si nous voulons améliorer la recherche sur les maladies neurodégénératives. Le but de cette thèse est d'utiliser la théorie psychométrique moderne: théorie de la réponse d’item pour analyser ces données catégoriques afin de mieux comprendre les maladies neurodégénératives et de faciliter la recherche de médicaments correspondante. Tout d'abord, nous avons appliqué l'analyse de Rasch afin d'évaluer la validité du score de neuropathie Charcot-Marie-Tooth (CMTNS), un critère important d'évaluation principal pour les essais cliniques de la maladie de CMT. Nous avons ensuite adapté le modèle Rasch à l'analyse des associations génétiques pour identifier les gènes associés à la maladie d'Alzheimer. Cette méthode résume les génotypes catégoriques de plusieurs marqueurs génétiques tels que les polymorphisme nucléotidique (SNPs) en un seul score génétique. Enfin, nous avons calculé l'information mutuelle basée sur la théorie de réponse d’item pour sélectionner les items sensibles dans ADAS-cog, une mesure de fonctionnement cognitif la plus utilisées dans les études de la maladie d'Alzheimer, afin de mieux évaluer le progrès de la maladie. / Neurodegenerative diseases, such as Alzheimer’s disease (AD) and Charcot Marie Tooth (CMT), are complex diseases. Their pathological mechanisms are still not well understood, and the progress in the research and development of new potential disease-modifying therapies is slow. Categorical data like rating scales and Genome-Wide Association Studies (GWAS) data are widely utilized in the neurodegenerative diseases in the diagnosis, prediction and progression monitor. It is important to understand and interpret these data correctly if we want to improve the disease research. The purpose of this thesis is to use the modern psychometric Item Response Theory to analyze these categorical data for better understanding the neurodegenerative diseases and facilitating the corresponding drug research. First, we applied the Rasch analysis in order to assess the validity of the Charcot-Marie-Tooth Neuropathy Score (CMTNS), a main endpoint for the CMT disease clinical trials. We then adapted the Rasch model to the analysis of genetic associations and used to identify genes associated with Alzheimer’s disease by summarizing the categorical genotypes of several genetic markers such as Single Nucleotide Polymorphisms (SNPs) into one genetic score. Finally, to select sensitive items in the most used psychometrical tests for Alzheimer’s disease, we calculated the mutual information based on the item response model to evaluate the sensitivity of each item on the ADAS-cog scale.

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