2 Taxonomy is always a contentious issue because the world does not come to us in neat little packages (S. Personality has been conceptualized from a variety of theoretical perspectives, and at various levels of Each of these levels has made unique contributions to our understanding of individual differences in behavior and experience. However, the number of personality traits, and scales designed to measure them, escalated without an end in sight (Goldberg, 1971). Researchers, as well as practitioners in the field of personality assessment, were faced with a bewildering array of personality scales from which to choose, with little guidance and no overall rationale at hand. What made matters worse was that scales with the same name often measure concepts that are not the same, and scales with different names often measure concepts that are quite similar. Although diversity and scientific pluralism are useful, the systematic accumulation of findings and the communication among researchers became difficult amidst the Babel of concepts and scales. Many personality researchers had hoped that they might devise the structure that would transform the Babel into a community speaking a common language. However, such an integration was not to be achieved by any one researcher or by any one theoretical perspective. As Allport once put it, " each assessor has his own pet units and uses a pet battery of diagnostic devices " (1958, p. 258). What personality psychology needed was a descriptive model, or taxonomy, of its subject matter. One of the central goals of scientific taxonomies is the definition of overarching domains within which large numbers of specific instances can be understood in a simplified way. Thus, in personality psychology, a taxonomy would permit researchers to study specified domains of personality characteristics, rather than examining separately the thousands of particular attributes that make human beings individual and unique. Moreover, a generally accepted taxonomy would greatly facilitate the accumulation and communication of empirical findings by offering a standard vocabulary, or nomenclature. After decades of research, the field is approaching consensus on a general taxonomy of personality traits, the " Big Five " personality dimensions. These dimensions do not represent a particular theoretical perspective but were derived from analyses of the natural-language terms people use to describe themselves 3 and others. Rather than replacing all previous systems, the Big Five taxonomy serves an integrative function because it can represent the various and diverse systems of personality …

The Big Five Trait taxonomy: History, measurement, and theoretical perspectives.

This personal historical article traces the development of the Big-Five factor structure, whose growing acceptance by personality researchers has profoundly influenced the scientific study of individual differences. The roots of this taxonomy lie in the lexical hypothesis and the insights of Sir Francis Galton, the prescience of L. L. Thurstone, the legacy of Raymond B. Cattell, and the seminal analyses of Tupes and Christal. Paradoxically, the present popularity of this model owes much to its many critics, each of whom tried to replace it, but failed. In reaction, there have been a number of attempts to assimilate other models into the five-factor structure. Lately, some practical implications of the emerging consensus can be seen in such contexts as personnel selection and classification.

/pdf/the-structure-of-phenotypic-personality-traits-2a2319vija.pdf

The Structure of Phenotypic Personality Traits

Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

On robust estimation of the location parameter

Measurement and control of response bias.

In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework refer to (a) the kind of covariates -- their mode (person, item, person-by-item), and their being external vs. internal to responses, and (b) the kind of effects the covariates have -- fixed vs. random, and if random, the mode across which the effects are random (persons, items). Based on this framework, three broad categories of models are described: Item covariate models, person covariate models, and person-by-item covariate models, and within each category three types of more specific models are discussed. The models in question are explained and the associated lmer code is given. Examples of models are the linear logistic test model with an error term, differential item functioning models, and local item dependency models. Because the lme4 package is for univariate generalized linear mixed models, neither the two-parameter, and three-parameter models, nor the item response models for polytomous response data, can be estimated with the lmer function.

https://pure.uva.nl/ws/files/1477268/104579_deBoeck_et_al_2011.pdf

The estimation of item response models with the lmer function from the lme4 package in R

Mixed models take the dependency between observations based on the same cluster into account by introducing 1 or more random effects Common item response theory (IRT) models introduce latent person variables to model the dependence between responses of the same participant Assuming a distribution for the latent variables, these IRT models are formally equivalent with nonlinear mixed models It is shown how a variety of IRT models can be formulated as particular instances of nonlinear mixed models The unifying framework offers the advantage that relations between different IRT models become explicit and that it is rather straightforward to see how existing IRT models can be adapted and extended The approach is illustrated with a self-report study on anger

/pdf/a-nonlinear-mixed-model-framework-for-item-response-theory-1t0wdc6kbj.pdf

A nonlinear mixed model framework for item response theory.

A discrete, categorical model and a corresponding data-analysis method are presented for two-way two-mode (objects × attributes) data arrays with 0, 1 entries. The model contains the following two basic components: a set-theoretical formulation of the relations among objects and attributes; a Boolean decomposition of the matrix. The set-theoretical formulation defines a subset of the possible decompositions as consistent with it. A general method for graphically representing the set-theoretical decomposition is described. The data-analysis algorithm, dubbed HICLAS, aims at recovering the underlying structure in a data matrix by minimizing the discrepancies between the data and the recovered structure. HICLAS is evaluated with a simulation study and two empirical applications.

Hierarchical classes: Model and data analysis

Explanatory Item Response Models

It is common practice in IRT to consider items as fixed and persons as random. Both, continuous and categorical person parameters are most often random variables, whereas for items only continuous parameters are used and they are commonly of the fixed type, although exceptions occur. It is shown in the present article that random item parameters make sense theoretically, and that in practice the random item approach is promising to handle several issues, such as the measurement of persons, the explanation of item difficulties, and trouble shooting with respect to DIF. In correspondence with these issues, three parts are included. All three rely on the Rasch model as the simplest model to study, and the same data set is used for all applications. First, it is shown that the Rasch model with fixed persons and random items is an interesting measurement model, both, in theory, and for its goodness of fit. Second, the linear logistic test model with an error term is introduced, so that the explanation of the item difficulties based on the item properties does not need to be perfect. Finally, two more models are presented: the random item profile model (RIP) and the random item mixture model (RIM). In the RIP, DIF is not considered a discrete phenomenon, and when a robust regression approach based on the RIP difficulties is applied, quite good DIF identification results are obtained. In the RIM, no prior anchor sets are defined, but instead a latent DIF class of items is used, so that posterior anchoring is realized (anchoring based on the item mixture). It is shown that both approaches are promising for the identification of DIF.

Paul De Boeck

Papers

The estimation of item response models with the lmer function from the lme4 package in R

A nonlinear mixed model framework for item response theory.

Hierarchical classes: Model and data analysis

Explanatory Item Response Models

Random Item IRT Models.