Browsing by Subject "Profile Analysis"

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    Adaptive profile difference analysis with applications to personality asessment
    (2024-12) Snodgress, Matthew
    The adaptive measurement of change (AMC) framework uses item response theory (IRT) and computerized adaptive testing (CAT) to detect psychometrically significant change between two or more occasions for a single individual. In recent years, AMC has been extended to include novel omnibus hypothesis tests for detecting change; multiple measurement occasions; polytomous IRT models; and multidimensional IRT models. In addition, numerous AMC studies support AMC’s ability to detect change with high accuracy. One unexplored application of AMC is to the detection of intra-individual, psychometrically significant differences among multiple traits for measurements obtained at a single occasion. Rather than administering one CAT on each occasion for a single trait, a CAT would instead be administered for each trait on one occasion with AMC’s hypothesis tests applied to detect significant differences among traits using IRT-based trait estimates. For example, if one individual’s score on a measure of Extraversion differs significantly from the same individual’s score on a measure of Agreeableness, knowing whether these two personality traits differ significantly could provide useful information about an individual’s personality tendencies. Extending the concept to all Big Five personality traits, understanding how such traits differ within a single person could be used to tailor job training or educational interventions. More generally, this procedure, denoted adaptive profile difference analysis (APDA), could improve the objective interpretation of multiscale assessments. In this study, AMC omnibus hypothesis tests were applied to detect intra-individual differences across multiple traits. A Monte Carlo simulation study was conducted using synthetic data based on three real personality datasets. Nine design factors were varied to examine APDA under various realistic conditions. Two primary outcome measures included the true positive rate (i.e., the proportion of true differences over the total number of detected differences) and the false positive rate (i.e., the proportion of detected differences that are significant under conditions where there is no true difference). Findings indicate that APDA is viable under certain conditions, particularly for personality multiscale assessments. Based on these results, recommendations for assessment design and future research are provided.
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    Interview codings of attachment style:using profile analysis to understand the patterns involved.
    (2011-01) Swinburne Romine, Rebecca Esther
    Attachment style is frequently discussed in terms of profiles of early childhood risk factors. Those using attachment interview methods use their ratings of these risk factors in developing an attachment style rating. In spite of this, profile analysis has yet to be used to model specific attachment styles. By using a multiple regression profile analysis to model attachment style in terms of coder's ratings of early risk factors, we can test empirically whether individual elements are relevant and how. The study of attachments began with Freud in the middle of the last century. Since that time attachment style has been studied first by John Bowlby and Mary Ainsworth, and since that time by many others. Early views of attachment, including the identification of specific attachment styles, and the investigation of its stability are discussed, as well as the limitations of the existing research. Specifically, the paper addresses the need for additional research to support or refute the theoretical models of attachment structure. Many methods have been developed to assess attachment style, most of which are closely tied to one particular theoretical view of attachment structure. Because the data for this paper are drawn from a study which utilized a four-prototype model of attachment as assessed for a coded semi-structured interview, the best way to understand the resultant codes is through a profile analysis. By using a two-step multiple regression profile analysis procedure, we can assess the unique contributions of both the level of risk, and the pattern of risk factors. The multiple regression methodology has the additional benefit of allowing for both continuous predictors and criterion variables; something that is not possible with other profile analysis methodologies. This allowed me to run the regressions with both dichotomous and semi-continuous criterion variables which enable the detection of different patterns. The results indicate that both the profile patterns and level can predict the criteria. The pattern component however is significantly more predictive of the criteria. While the derived patterns differ from the predicted patterns, they remain consistent with theory. Overall, environmental risk factors such as abuse, neglect, and parental rejection were not predictive of attachment style or score, while individual risk factors such as anger at parents and rebellion, and the interactive factor of role reversal were highly predictive. This leads us to conclude that profiles are a viable method of understanding attachment styles, and that it is the individual's responses to the risk factors present in the childhood environment rather than those factors themselves which determine attachment style.