The purpose was to evaluate the psychometric properties of an 8-item questionnaire instrument to support member checks in positivistic qualitative research. A sample of 1603 junior-level university nursing students (98.2% participation rate) a completed the questionnaire instrument which assessed how closely a written description of the lived experience of in-hospital training applied to their personal experience. The instrument had excellent psychometric properties, including high measurement reliability (α=0.94), strong concurrent validity (72.2%-77.4%), discriminant validity (p < 0.000001), a parametric score distribution with little kurtosis and no skew, and an average score (G score) was centered at midpoint of the scale (midpoint of 4 on a scale from 1 to 7). The experience match was at the level of “matches experience to high degree,” showing the transferability (generalizability) of the findings to the present sample. The instrument can be used to assess the credibility and transferability of findings from qualitative research, assist in finding negative cases, determine the degree of saturation and success of bracketing, and complement the constant comparative method. The instrument is recommended for general use in positivistic qualitative or naturalistic inquiry studies for any type of sample and any type of lived experience. This approach would magnify and empower the reach of the products of qualitative research.

Objective: To test the psychometric properties, internal consistency, dimensional structure, and convergent validity of the German version of the Demoralization Scale- II (DS-II), and to examine the association between demoralization, sociodemographic, disease- and treatment-related variables in patients with cancer. Methods: We recruited adult patients with cancer at a Psychosocial Counseling Center and at oncological wards. Participants completed the 16-item DS-II, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Screener-2 (GAD-2), Distress Thermometer (DT), and Body Image Scale (BIS). We analyzed internal consistency of the DS-II using Cronbach‘s Alpha (a). We tested the dimensional structure of the DS-II with Confirmatory Factor Analyses (CFA). Convergent validity was expressed through correlation coefficients with established measures of psychological distress. The associations between demoralization, sociodemographic, disease- and treatmentrelated variables were examined with ANOVAs. Results: Out of 942 eligible patients, 620 participated. The average DS-II total score was M = 5.78, SD = 6.34, the Meaning and Purpose subscale M = 2.20, SD = 3.20, and the Distress and Coping Ability subscale M = 3.58, SD = 3.45. Internal consistency ranged from high to excellent with a = 0.93 for the DS-II total scale, a = 0.90 for the Meaning and Purpose subscale, and a = 0.87 for the Distress and Coping Ability subscale. The one-factor and the two-factor model yielded similar model fits, with CFI and TLI ranging between 0.910 and 0.933, SRMR < 0.05. The DS-II correlated significantly with depression (PHQ-9: r = 0.69), anxiety (GAD-2: r = 0.72), mental distress (DT: r = 0.36), and body image disturbance (BIS: r = 0.58). High levels of demoralization were reported by patients aged between 18 and 49 years (M = 7.77, SD = 6.26), patients who were divorced/separated (M = 7.64, SD = 7.29), lung cancer patients (M = 9.29, SD = 8.20), and those receiving no radiotherapy (M = 7.46, SD = 6.60). Conclusion: The DS-II has very good psychometric properties and can be recommended as a reliable tool for assessing demoralization in patients with cancer. The results support the implementation of a screening for demoralization in specific risk groups due to significantly increased demoralization scores.

In psychometrics, a concern is that the assessment is fair for all students who take it. The fairness of an assessment can be evaluated in several ways, including the examination of differential item functioning (DIF). An item exhibits DIF if a subgroup has a lower probability of answering an item correctly than another subgroup after matching on academic achievement. Subgroups include race, spoken language, disability status, or sex. Under item response theory (IRT), a single score is given to each student since IRT assumes that an assessment is only measuring one construct. However, under cognitive diagnostic modeling (CDM), an assessment measures multiple specific constructs and classifies students as having mastered the construct or not. There are several methods to detect DIF under both types of models, but most methods cannot conduct explanatory modeling. Explanatory modeling consists of predicting item responses and latent traits using relevant observed or latent covariates. If an item exhibits DIF which disadvantages a subgroup, covariates can be modeled to explain the DIF and indicate either true or spurious differences. If an item exhibited statistically significant DIF which became nonsignificant after modeling explanatory variables, then the DIF would be explained and considered spurious. If the DIF remained significant after modeling explanatory variables, then there was stronger evidence that DIF was present and not spurious. When an item exhibits DIF, the validity of the inferences from the assessment is threatened and group comparisons become inappropriate. This study evaluated the presence of DIF on the Trends in International Math and Science Study (TIMSS) between students who speak English as a first language (EFL) and students who do not speak English as a first language (multilingual learners [ML]) in the USA. The 8th grade science data was analyzed from the year 2011 since science achievement remains understudied, the 8th grade is a critical turning point for K-12 students, and because 2011 was the most recent year that item content is available from this assessment. The item response theory with covariates (IRT-C) model was used as the explanatory IRT model, while the reparameterized deterministic-input, noisy "and" gate (RDINA) model was used as the explanatory CDM (E-CDM). All released items were analyzed for DIF by both models with language status as the key grouping variable. Items that exhibited significant DIF were further analyzed by including relevant covariates. Then, if items still exhibited DIF, their content was evaluated to determine why a group was disadvantaged. Several items exhibited significant DIF under both the IRT-C and E-CDM. Most disadvantaged ML students. Under the IRT-C, two items that exhibited DIF were explained by quantitative covariates. Two items that did not exhibit significant nonuniform DIF became significant after explanation. Whether or not a student repeated elementary school was the strongest explanatory covariate, while confidence in science explained the most items. Under the E-CDM, five items initially exhibited significant uniform DIF with one also exhibiting nonuniform DIF. After scale purification, two items exhibited significant uniform DIF, and one exhibited marginally significant DIF. After explanatory modeling, no items exhibited significant uniform DIF, and only one item exhibited marginally significant nonuniform DIF. Examining covariates, home educational resources explained the most with ten items and the strongest positive covariate. Repeated elementary school had the strongest absolute effect. Examining the item content of 14 items, most items had no causal explanation for the presence of DIF. In four items, a causal mechanism was identified and concluded to exhibit item bias. An item's cognitive domain had a relationship with DIF items, with 79% of items under the Knowing domain. Based on these results, DIF that disadvantaged ML students was present among several items on this science assessment. Both the IRT-C and E-CDM identified several items exhibiting DIF, quantitative covariates explained several items exhibiting DIF, and item bias was discovered in several items. Following up on this empirical study, a simulation study was performed to evaluate DIF detection power and Type I error rates of the Wald test and likelihood ratio (LR) test, and parameter recovery when ignoring subgroups, using the compensatory reparameterized unified model (C-RUM). Factors included sample size, DIF magnitude, DIF type, Q-matrix complexity, their interaction effects, and p-value adjustment. Evaluating DIF under the C-RUM, the DIF detection method had the largest effect on Type I error rates, with the Wald test recovering the nominal p-value much better than the LR test. In terms of power, DIF magnitude was the most important factor, followed by Q-matrix complexity. As DIF magnitude increased and Q-matrix complexity decreased, power rates increased. In terms of parameter recovery, the DIF type had the strongest effect, followed by Q-matrix complexity. Nonuniform DIF recovered the parameter more than uniform DIF, while fewer attributes measured by an item improved parameter recovery. Several factors affected DIF detection power and Type I error, including DIF detection method, DIF magnitude, and Q-matrix complexity. For parameter recovery, DIF type had an impact, along with Q-matrix complexity, and DIF magnitude. / Doctor of Philosophy / Academic assessments are a necessary tool to evaluate student educational progress in different subjects across school years. These are necessary to establish student proficiency within schools, districts, states, and countries. The results can be broken down to make various comparisons, including by race, ethnicity, gender, language status, schools, or any other demographic. Other comparisons can be made against a proficiency standard or passing rate. It is important and necessary to make comparisons between groups so that any disparities or achievement gaps can be identified and rectified. This study evaluated achievement gaps between multilingual learner (ML) students and English first language (EFL) students on individual items of an 8th-grade international science assessment. This subject and grade level are crucial for students preparing for college and starting their career development. Every test item was analyzed to determine if there was an achievement gap and if an item was biased against a group based on their first language. Several follow-up analyses were conducted on every item to ensure that the results were as accurate as possible and that there were no other plausible explanations. Several explanatory factors were evaluated, including student home educational resources, confidence in science, likes learning science, repeating elementary school, being bullied at school, and time spent on science homework. For items that had achievement gaps based on language, further analysis was conducted to ensure that the gaps were not due to other student characteristics. Based on that analysis, the item content was examined by myself and a content expert. This was done to evaluate if there were characteristics of the item that led to the language achievement gap. This allowed for the evaluation of whether an item was biased against either ML or EFL students. Fourteen items exhibited achievement gaps based on language status. Most items disadvantaged ML students, and the achievement gaps ranged from small to large. This initial analysis was followed up with more extensive analyses to rule out other potential causes of the achievement gaps. Repeated elementary school had the strongest relationship with these items, while confidence in science was related to the most items exhibiting achievement gaps. There were two items in which the language achievement gap was explained by a combination of factors, thus concluding that there was not any gap on the items. The remaining items still exhibited achievement gaps which led to analysis of the item content. On four items, the causes of the remaining achievement gaps were discovered. For the remaining items, there was no clear reason for the item bias and achievement gaps. This study was followed by a study to evaluate a new method of detecting achievement gaps. This was done by creating specific data so that the true values were known. The sample size, test item complexity, achievement gap size and direction, and gap detection method were evaluated. These conditions and their values were chosen to reflect realistic testing scenarios and provide a better understanding of the previous study's results. The results indicated that one achievement gap detection method had higher detection rates compared to the other detection method. This was true in all conditions. Additionally, achievement gaps were found more often when sample sizes and achievement gaps were larger, test items were less complex, and when one group was disadvantaged across all ability levels. When comparing the estimated and true statistics, there were large deviations when one group was disadvantaged at different proficiency levels. Also, when items were more complex, and sample sizes were smaller, the deviation between true and estimated statistics was larger than when items were simpler and sample sizes were larger.

With advances in computer technology and expanded access to educational data, psychometrics faces new opportunities and challenges for enhancing pattern discovery and decision-making in testing and learning. In this dissertation, I introduced three computational psychometrics studies for solving the technical problems in item-based computerized adaptive learning (CAL) systems related to dynamic measurement, diagnosis, and recommendation based on Bayesian item response theory (IRT). For the first study, I introduced a new knowledge tracing (KT) model, dynamic IRT (DIRT), which can iteratively update the posterior distribution of latent ability based on moment match approximation and capture the uncertainty of ability change during the learning process. For dynamic measurement, DIRT has advantages in interpretation, flexibility, computation cost, and implementability. For the second study, A new measurement model, named multilevel and multidimensional item response theory with Q matrix (MMIRT-Q), was proposed to provide fine-grained diagnostic feedback. I introduced sequential Monte Carlo (SMC) for online estimation of latent abilities. For the third study, I proposed the maximum expected ratio of posterior variance reduction criterion (MERPV) for testing purposes and the maximum expected improvement in posterior mean (MEIPM) criterion for learning purposes under the unified framework of IRT. With these computational psychometrics solutions, we can improve the students’ learning and testing experience with accurate psychometrics measurement, timely diagnosis feedback, and efficient item selection.

In recent decades, both global migration in general and specifically migration to Sweden have increased. This development compels the need for delivering healthcare to the increasingly diverse populations in Sweden. To support health professionals, for instance occupational therapists, in developing their professional knowledge in encounters with foreign-born clients a self-rating instrument measuring cultural competence is developed. This may contribute to the development of suitable services for foreignborn clients and improve person-centered interventions for these clients. The general aim of this thesis was to develop an instrument for health professionals by examining psychometric properties and utility of the Swedish version of the Cultural Competence Assessment Instrument (CCAI-S) among occupational therapists. The specific aim of study I was to evaluate the content validity and utility of the Swedish version of the Cultural Competence Assessment Instrument (CCAI-S) among occupational therapists. The study had a descriptive and explorative design. Nineteen occupational therapists participated, divided into four focus groups. Qualitative content analysis was used to examine the content validity and utility of the CCAI-S. The specific aim of study II was to examine the clinical relevance, construct validity and reliability of the Swedish version of the Cultural Competence Assessment Instrument (CCAI-S) among Swedish occupational therapists. The study had a cross-sectional design. A web-based questionnaire was e-mailed to a randomised sample of 428 occupational therapists to investigate the construct validity, reliability and utility of the CCAI-S. Factor analysis was performed as well as descriptive statistics. The findings from study I revealed high content validity for all 24 items. However, six items needed reformulations and exemplifications. Regarding utility, the results showed strong support for CCAI-S. The category ‘Interactions with clients’ showed that the CCAI-S could be utilised individually for the health professional and create a higher awareness of cultural questions in practice. The category ‘Workplace and its organisational support’ displayed potential for use in different workplaces regarding CCAI-S and indicated the importance of organisational support for health professionals in the development of cultural competence. The findings from study II regarding construct validity generated a three-factor model with the labels ‘Openness and awareness’, ‘Workplace support’ and ‘Interaction skills’. All three factors showed high factor loadings and contained 12 of the 24 original items. The Cronbach’s Alpha showed high support for the three-factor model. Concerning utility, the participants reported that all 24 items had high clinical relevance. In conclusion, the findings from the two studies indicated good measurement properties and high clinical relevance for the CCAI-S. This may sup-port the utilisation of CCAI-S in the Swedish context for health professionals, for instance occupational therapists. The results of the instrument development show that the upcoming published version of the CCAI-S can be a valuable self-assessment tool for health professionals who strive to improve in person-centred communication in encounters with foreign-born clients. CCAI-S can also be of support for the organisation to serve as a guide for what to focus on to develop cultural competence within the staff. Altogether this presumably influence the effectiveness of the healthcare and enhance the evidence of interventions for foreign-born clients. To develop an instrument is an iterative process requiring several evaluations and tests in various settings and populations. Therefore further psychometric testing and utility studies on the CCAI-S is crucial.

Objectives: 1) To examine the effectiveness of Audiovisual (AV) interventions at reducing preoperative anxiety (PA) and its associated outcomes in children undergoing surgery; 2) To assess the psychometric properties of a new scale, the Children’s Perioperative Multidimensional Anxiety Scale (CPMAS); 3) To examine the feasibility and acceptability of a novel tablet-based intervention, Story-Telling Medicine (STM), for reducing children’s PA; 4) To examine the relation between temperament and PA in the surgical context. Methods: A systematic review of studies where the primary outcome was children’s PA was conducted (Study 1). A study of the reliability and validity of the CPMAS were assessed at preoperative assessment (T1), on the day of surgery (T2), and 1 month postoperatively (T3) was also undertaken (Study 2). The feasibility and acceptability of STM were then examined and compared its effect to Usual Care (UC) (Study 3). Finally, children’s temperament was examined using the Colorado Childhood Temperament Inventory at T1 along with the CPMAS assessed at T1 and T2 (Study 4). Results: Fourteen of the 18 studies led to reductions in children’s PA (Study 1). The CPMAS demonstrated good internal consistency, stability and convergent validity across all visits (Study 2). The participant recruitment and study procedures were shown to be feasible and children in the STM group demonstrated greater reductions in CPMAS compared to the UC group (Study 3). Shyness predicted lower PA at T1, while sociability predicted higher PA at T1 and T2 (Study 4). Conclusions: AV interventions appear useful but full-scale RCTs of these treatments are required to pinpoint those that are most effective. The CPMAS is a promising tool for evaluating children’s PA and preliminary evidence suggests that STM is a feasible intervention for reducing children's PA. Finally, our findings highlight the importance of considering individual differences in predicting anxiety in the surgical setting. / Thesis / Doctor of Philosophy (PhD) / Preoperative anxiety affects up to 5 million children in North America annually and is associated with many adverse psychological, behavioural, and health effects. These problems not only produce significant distress for children and families, but can have immediate and long-term effects on a child’s mental health and development. Current methods to reduce preoperative anxiety in children (e.g., medications and psychological preparation programs) are limited by their expense and/or time-intensive nature. In an attempt to reduce children’s preoperative anxiety and its associated negative outcomes, this work examined the usefulness of audiovisual interventions in reducing children’s preoperative anxiety, sought to develop a new instrument to measure children’s preoperative anxiety, tested a new tablet-based application to reduce children’s preoperative anxiety, and examined the relation between children’s temperament and preoperative anxiety. The goal is to improve the surgical experience for children and families, and to reduce psychological and physical problems in children undergoing surgery.

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