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Principal component analysis (PCA) is a widely used tool for establishing the dimensional structure in questionnaire data. Whenever questionnaire data are incomplete, the missing data need to be treated prior to carrying out a PCA. Several methods exist for handling missing data prior to carrying out a PCA. The current chapter first discusses the most recent developments regarding the treatment of missing data in PCA. Next, of these methods, the method that is most promising both from a theoretical and practical point of view will be discussed in more detail, namely, multiple imputation. Finally, some extensions of multiple imputation to other PCA-related techniques or to statistics within PCA beyond the basics are discussed, and some general recommendations regarding the use of PCA on multiply imputed datasets in different statistical software packages will be given.

OBJECTIVE: Many individuals with an eating disorder do not receive appropriate care. Low-threshold interventions could help bridge this treatment gap. The study aim was to evaluate the effectiveness of Featback, a fully automated online self-help intervention, online expert-patient support and their combination. METHOD: A randomized controlled trial with a 12-month follow-up period was conducted. Participants aged 16 or older with at least mild eating disorder symptoms were randomized to four conditions: (1) Featback, a fully automated online self-help intervention, (2) chat or email support from a recovered expert patient, (3) Featback with expert-patient support and (4) a waiting list control condition. The intervention period was 8 weeks and there was a total of six online assessments. The main outcome constituted reduction of eating disorder symptoms over time. RESULTS: Three hundred fifty five participants, of whom 43% had never received eating disorder treatment, were randomized. The three active interventions were superior to a waitlist in reducing eating disorder symptoms (d = -0.38), with no significant difference in effectiveness between the three interventions. Participants in conditions with expert-patient support were more satisfied with the intervention. DISCUSSION: Internet-based self-help, expert-patient support and their combination were effective in reducing eating disorder symptoms compared to a waiting list control condition. Guidance improved satisfaction with the internet intervention but not its effectiveness. Low-threshold interventions such as Featback and expert-patient support can reduce eating disorder symptoms and reach the large group of underserved individuals, complementing existing forms of eating disorder treatment. PUBLIC SIGNIFICANCE STATEMENT: Individuals with eating-related problems who received (1) a fully automated internet-based intervention, (2) chat and e-mail support by a recovered individual or (3) their combination, experienced stronger reductions in eating disorder symptoms than those who received (4) usual care. Such brief and easy-access interventions play an important role in reaching individuals who are currently not reached by other forms of treatment. ispartof: INTERNATIONAL JOURNAL OF EATING DISORDERS vol:55 issue:10 pages:1361-1373 ispartof: location:United States status: published

A balanced ANOVA design provides an unambiguous interpretation of the F-tests, and has more power than an unbalanced design. In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type-III sum of squares. In the current simulation study we studied four pooled statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data, and compared them with Type-III sum of squares. Statistics D₁ and D₂ generally performed best regarding Type-I error rates, and had power rates closest to that of Type-III sum of squares. Additionally, for the interaction, D₁ produced power rates higher than Type-III sum of squares. For multiply imputed datasets D₁ and D₂ may be the best methods for pooling the results in multiply imputed datasets, and for unbalanced data, D₁ might be a good alternative to Type-III sum of squares regarding the interaction.

Objective: The primary aim was assessing the cost-effectiveness of an internet-based self-help program, expert-patient support, and the combination of both compared to a care-as-usual condition. Method: An economic evaluation from a societal perspective was conducted alongside a randomized controlled trial. Participants aged 16 or older with at least mild eating disorder symptoms were randomly assigned to four conditions: (1) Featback, an online unguided self-help program, (2) chat or e-mail support from a recovered expert patient, (3) Featback with expert-patient support, and (4) care-as-usual. After a baseline assessment and intervention period of 8 weeks, five online assessments were conducted over 12 months of follow-up. The main result constituted cost-utility acceptability curves with quality-of-life adjusted life years (QALYs) and societal costs over the entire study duration. Results: No significant differences between the conditions were found regarding QALYs, health care costs and societal costs. Nonsignificant differences in QALYs were in favor of the Featback conditions and the lowest societal costs per participant were observed in the Featback only condition (euro16,741) while the highest costs were seen in the care-as-usual condition (euro 28,479). The Featback only condition had the highest probability of being efficient compared to the alternatives for all acceptable willingness-to-pay values. Discussion: Featback, an internet-based unguided self-help intervention, was likely to be efficient compared to Featback with guidance from an expert patient, guidance alone and a care-as-usual condition. Results suggest that scalable interventions such as Featback may reduce health care costs and help individuals with eating disorders that are currently not reached by other forms of treatment. Public significance statement Internet-based interventions for eating disorders might reach individuals in society who currently do not receive appropriate treatment at low costs. Featback, an online automated self-help program for eating disorders, was found to improve quality of life slightly while reducing costs for society, compared to a do-nothing approach. Consequently, implementing internet-based interventions such as Featback likely benefits both individuals suffering from an eating disorder and society as a whole.

Whenever statistical analyses are applied to multiply imputed datasets, specific formulas are needed to combine the results into one overall analysis, also called combination rules. In the context of regression analysis, combination rules for the unstandardized regression coefficients, the t-tests of the regression coefficients, and the F-tests for testing $$R^{2}$$R2 for significance have long been established. However, there is still no general agreement on how to combine the point estimators of $$R^{2}$$R2 in multiple regression applied to multiply imputed datasets. Additionally, no combination rules for standardized regression coefficients and their confidence intervals seem to have been developed at all. In the current article, two sets of combination rules for the standardized regression coefficients and their confidence intervals are proposed, and their statistical properties are discussed. Additionally, two improved point estimators of $$R^{2}$$R2 in multiply imputed data are proposed, which in their computation use the pooled standardized regression coefficients. Simulations show that the proposed pooled standardized coefficients produce only small bias and that their 95% confidence intervals produce coverage close to the theoretical 95%. Furthermore, the simulations show that the newly proposed pooled estimates for $$R^{2}$$R2 are less biased than two earlier proposed pooled estimates.

Whenever multiple regression is applied to a multiply imputed data set, several methods for combining significance tests for R2 and the change in R2 across imputed data sets may be used: the combination rules by Rubin, the Fisher z-test for R2 by Harel, and F-tests for the change in R2 by Chaurasia and Harel. For pooling R2 itself, Harel proposed a method based on a Fisher z transformation. In the current article, it is argued that the pooled R2 based on the Fisher z transformation, the Fisher z-test for R2, and the F-test for the change in R2 have some theoretical flaws. An argument is made for using Rubin’s method for pooling significance tests for R2 instead, and alternative procedures for pooling R2 are proposed: simple averaging and a pooled R2 constructed from the pooled significance test by Rubin. Simulations show that the Fisher z-test and Chaurasia and Harel’s F-tests generally give inflated type-I error rates, whereas the type-I error rates of Rubin’s method are correct. Of the methods for pooling the point estimates of R2 no method clearly performs best, but it is argued that the average of R2’s across imputed data set is preferred.

BackgroundAntisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined.MethodsWe harmonized data from 1489 participants (9–27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood.ResultsThree dimensions of antisociality were uncovered: ‘callous-unemotional (CU)/manipulative traits’, ‘intentional aggression/conduct’, and ‘reactivity/impulsivity/irritability’. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age.ConclusionsAcross a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations.

Analyses of the present data are reported in the article “Crossing Boundaries: A Pilot Study of Maternal Attitudes about Child Maltreatment in Nine Countries” [8]. Data were collected during home visits using the Maltreatment Q-Sort (MQS). A total of 466 mothers from nine different countries gave their opinion about child maltreatment by sorting 90 cards with parenting behaviors taken from the literature that reflect four types of child maltreatment, into 9 evenly distributed stacks (with 10 cards each) from least to most harmful for the child. This data article provides an overview of the content of the 90 items, which type of maltreatment they reflect, and the source of the items. The percentage of mothers labelling each of the MQS items as maltreatment is also presented. In addition, instructions are included about the administration of the MQS as well as data-entry and analyses of Q-sort data, accompanied by example datasets and syn- taxes. This can serve as a manual for researchers interested in using Q-sort data., (undefined)

This study aimed to find the optimal filter slope for cochlear implant simulations (vocoding) by testing the effect of a wide range of slopes on the discrimination of emotional and linguistic (focus) prosody, with varying availability of F0 and duration cues. Forty normally hearing participants judged if (non-)vocoded sentences were pronounced with happy or sad emotion, or with adjectival or nominal focus. Sentences were recorded as natural stimuli and manipulated to contain only emotion- or focus-relevant segmental duration or F0 information or both, and then noise-vocoded with 5, 20, 80, 120, and 160 dB/octave filter slopes. Performance increased with steeper slopes, but only up to 120 dB/octave, with bigger effects for emotion than for focus perception. For emotion, results with both cues most closely resembled results with F0, while for focus results with both cues most closely resembled those with duration, showing emotion perception relies primarily on F0, and focus perception on duration. This suggests that filter slopes affect focus perception less than emotion perception because for emotion, F0 is both more informative and more affected. The performance increase until extreme filter slope values suggests that much performance improvement in prosody perception is still to be gained for CI users.

Whenever statistical analyses are applied to multiply imputed datasets, specific formulas are needed to combine the results into one overall analysis, also called combination rules. In the context of regression analysis, combination rules for the unstandardized regression coefficients, the t-tests of the regression coefficients, and the F-tests for testing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 for significance have long been established. However, there is still no general agreement on how to combine the point estimators of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 in multiple regression applied to multiply imputed datasets. Additionally, no combination rules for standardized regression coefficients and their confidence intervals seem to have been developed at all. In the current article, two sets of combination rules for the standardized regression coefficients and their confidence intervals are proposed, and their statistical properties are discussed. Additionally, two improved point estimators of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 in multiply imputed data are proposed, which in their computation use the pooled standardized regression coefficients. Simulations show that the proposed pooled standardized coefficients produce only small bias and that their 95% confidence intervals produce coverage close to the theoretical 95%. Furthermore, the simulations show that the newly proposed pooled estimates for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 are less biased than two earlier proposed pooled estimates. Electronic supplementary material The online version of this article (10.1007/s11336-020-09696-4) contains supplementary material, which is available to authorized users.

Missing data is a problem that occurs frequently in many scientific areas. The most sophisticated method for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem of systematic dropout. Although from a theoretical point of view multiple imputation is considered to be the optimal method, many applied researchers are reluctant to use it because of persistent misconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically at rebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation.

Empirical research has shown an elevated risk for externalizing behavior problems in international adoptees. To address the extent to which this risk exists for more serious externalizing problems we compared the rates of registered criminal offending of internationally adopted adolescents with those of non-adopted adolescents in the Netherlands. In a large population-based cohort study (N = 3,758,506 including n = 10,563 international adoptees) on Dutch youth with ages up to 19 years we examined registrations in the program on juvenile crime and in the national police system from 2005 to 2013. Controlling for time lapse and background variables we found that international adoptees had been in contact with the criminal justice system more frequently than non-adoptees. However, the findings differed across region of adoption: Adoptees from South America and from Africa had been in contact with the criminal justice system most frequently (and more often than non-adoptees), whereas adoptees from China (total n = 4569) had the least contacts (and less often than non-adoptees). The percentages of criminal offending of adoptees ranged between 1.16% and 15.83% across regions of adoption (versus 10.86% in non-adoptees). The large majority of adoptees – including those from South America and Africa – were not involved in criminal acts. We hypothesize that the higher and lower risks of criminal offending found for adoptees from certain countries are associated with the varying levels of pre-adoption adversity (e.g., neglect and abuse) that the adoptees have experienced.

Children in foster care are often characterized by low academic outcomes which negatively impact their later lives. School engagement may be a key element to promote their academic and educational outcomes. However, little is known about the development of school engagement in foster children and longitudinal studies are lacking. The current study reports the findings of a three-wave longitudinal study wherein we examined the development of school engagement and analyzed which factors were predictive of school engagement in a sample of 363 Dutch foster children (age range 5–18 years, 46.6% girls). Multilevel analyses showed that characteristics related to demographics, school functioning, foster children, and foster families predicted levels of school engagement of children in foster care. Foster children's behavioral functioning and foster parents’ positive parenting appeared to be characteristics important to consider in screening and interventions. Based on the findings we suggest that teachers and foster care professionals should collaborate to ensure that school engagement and consequently school functioning becomes part of foster children's personal development plans.

BackgroundThe One Drop | Mobile app supports manual and passive (via HealthKit and One Drop’s glucose meter) tracking of self-care and glycated hemoglobin A1c (HbA1c). ObjectiveWe assessed the HbA1c change of a sample of people with type 1 diabetes (T1D) or type 2 diabetes (T2D) using the One Drop | Mobile app on iPhone and Apple Watch, and tested relationships between self-care tracking with the app and HbA1c change. MethodsIn June 2017, we identified people with diabetes using the One Drop | Mobile app on iPhone and Apple Watch who entered two HbA1c measurements in the app 60 to 365 days apart. We assessed the relationship between using the app and HbA1c change. ResultsUsers had T1D (n=65) or T2D (n=191), were 22.7% (58/219) female, with diabetes for a mean 8.34 (SD 8.79) years, and tracked a mean 2176.35 (SD 3430.23) self-care activities between HbA1c entries. There was a significant 1.36% or 14.9 mmol/mol HbA1c reduction (F=62.60, P

Background: Three recent reviews evaluated 19 studies testing the hemoglobin A1c (HbA1c) benefit of 16 diabetes apps, including 5 publicly available apps. Most studies relied on small samples and did not link app engagement with outcomes. Objective: This study assessed both HbA1c change in a large sample of people using the One Drop | Mobile app and associations between app engagement and changes in HbA1c. Methods: The One Drop | Mobile app for iOS and Android is designed to manually and passively (via Apple HealthKit, Google Fit, and the One Drop | Chrome blood glucose meter) store, track, and share data. Users can schedule medication reminders, view statistics, set goals, track health outcomes, and get data-driven insights. In June 2017, we queried data on people with diabetes using the app who had entered at least 2 HbA1c values in the app >60 and ≤365 days apart. Multiple imputation corrected for missing data. Unadjusted and adjusted mixed effects repeated measures models tested mean HbA1c change by time, diabetes type, and their interaction. Multiple regression models assessed relationships between using the app to track food, activity, blood glucose, and medications and HbA1c change. Results: The sample (N=1288) included people with type 1 diabetes (T1D) (n=367) or type 2 diabetes (T2D) (n=921) who were 35% female, diagnosed with diabetes for a mean 9.4 (SD 9.9) years, and tracked an average 1646.1 (SD 3621.9) self-care activities in One Drop | Mobile between their first (mean 8.14% [SD 2.06%]) and second HbA1c entry (mean 6.98% [SD 1.1%]). HbA1c values were significantly associated with user-entered average blood glucose 90 days before the second HbA1c entry (rho=.73 to .75, P

Background: definitions of child maltreatment vary widely between studies, and even more so between different cultural contexts. Objective: in this pilot study, we examine between-country variations in maternal notions about what constitutes child maltreatment. Participants and setting: the sample consisted of 466 mothers recruited in Chile, China, Greece, Iran, the Netherlands, Portugal, South Africa, Turkey, and Uruguay. Methods: all mothers completed a new Q-sort measure, ranking 90 parenting behaviors linked to subtypes of maltreatment (emotional neglect, emotional abuse, physical neglect, and physical abuse) from least to most detrimental to child development. Results: between-country agreement regarding the harmfulness of the parenting behaviors was high (r=.45), but there were different patterns of reported harmfulness of subtypes of maltreatment (although driven mostly by deviating patterns in the South African sample). Further, there were significant country effects on the number and type of behaviors labeled as maltreatment (pre=.15), and the number of items labeled as requiring intervention (pre=.19). Conclusions: variations in conceptions of maltreatment need to be studied in larger more representative samples and taken into account in the assessment and treatment of child maltreatment across cultures., NA

As a procedure for handling missing data, Multiple imputation consists of estimating the missing data multiple times to create several complete versions of an incomplete data set. All these data sets are analyzed by the same statistical procedure, and the results are pooled for interpretation. So far, no explicit rules for pooling F tests of (repeated-measures) analysis of variance have been defined. In this article we outline the appropriate procedure for the results of analysis of variance (ANOVA) for multiply imputed data sets. It involves both reformulation of the ANOVA model as a regression model using effect coding of the predictors and applying already existing combination rules for regression models. The proposed procedure is illustrated using 3 example data sets. The pooled results of these 3 examples provide plausible F and p values.

Principal component analysis (PCA) is a widely used statistical technique for determining subscales in questionnaire data. As in any other statistical technique, missing data may both complicate its execution and interpretation. In this study, six methods for dealing with missing data in the context of PCA are reviewed and compared: listwise deletion (LD), pairwise deletion, the missing data passive approach, regularized PCA, the expectation-maximization algorithm, and multiple imputation. Simulations show that except for LD, all methods give about equally good results for realistic percentages of missing data. Therefore, the choice of a procedure can be based on the ease of application or purely the convenience of availability of a technique.

Empirical evidence has shown that international adoptees present physical growth delays, precocious puberty, behavioral problems, and mental health referrals more often than non-adoptees. We hypothesized that the higher prevalence of (mental) health problems in adoptees is accompanied by elevated consumption of prescription drugs, including antidepressants, attention deficit hyperactivity disorder (ADHD) medication, and medication for growth inhibition/stimulation. In an archival, population-based Dutch cohort study, data on medication use were available from the Health Care Insurance Board by Statistics Netherlands from 2006 to 2011. The Dutch population born between 1994 and 2005 and alive during the period of measurement was included (2,360,450 including 10,602 international adoptees, of which 4447 from China). Their mean age was 6.5 years at start (range 1–12 years) and 11.5 years at the end of the measurement period (range 6–17 years). Chinese female adoptees used less medication for precocious puberty (as treatment for precocious puberty; odds ratio (OR) = 0.57, effect size Cohen’s d = −0.31) and contraception (OR = 0.65, d = −0.24) than non-adoptees. For both males and females, non-Chinese adoptees used more medication for ADHD than non-adoptees (males: OR = 1.22, females: OR = 1.32), but the effect was small (males: d = 0.11, females: d = 0.15). Conclusions: Adoptees in the Netherlands generally do not use more medication than their non-adopted peers. What is Known: • Meta-analytical evidence shows that international adoptees present physical growth delays and mental health referrals more often than non-adopted controls. • With the exception of one Swedish study on ADHD medication, there is no other systematic research on medication use of international adoptees. What is New: • All differences in medication use between international adoptees in the Netherlands and non-adopted controls were below the threshold of a small effect with the exception of medication for precocious puberty, but this effect was in the opposite direction with female adoptees using less medication for precocious puberty than non-adoptees. • International adoptees in the Netherlands do not use more medication despite experiences of preadoption adversity and higher rates of mental health referrals during childhood and adolescence.

We examined associations of disorganized attachment and maternal depressive symptoms with infant autonomic functioning in 450 infant-mother dyads enrolled in the Generation R study. Maternal depressive symptoms were measured 2 months postpartum with the Brief Symptom Inventory. At 14 months, we assessed infant attachment with a slightly shortened Strange Situation and measured infant resting heart rate. Respiratory sinus arrhythmia (RSA) was calculated using spectral analysis. Higher levels of maternal postnatal depressive symptoms predicted lower resting RSA in disorganized infants (B=-0.31, SE=0.15, p=.04, R2=.05) but not in nondisorganized infants (B=0.05, SE=0.06, p=.36). This effect was buffered in disorganized infants with a secondary secure attachment classification. Disorganized infants were more vulnerable to the effect of maternal postnatal depressive symptoms on the physiological stress systems.

The performance of multiple imputation in questionnaire data has been studied in various simulation studies. However, in practice, questionnaire data are usually more complex than simulated data. For example, items may be counterindicative or may have unacceptably low factor loadings on every subscale, or completely missing subscales may complicate computations. In this article, it was studied how well multiple imputation recovered the results of several psychometrically important statistics in a data set with such properties. Analysis of this data set revealed that multiple imputation was able to recover the results of these analyses well. Also, a simulation study showed that multiple imputation produced small bias in these statistics for simulated data sets with the same properties.

The focus of this study was the incidence of different kinds of missing-data problems in personality research and the handling of these problems. Missing-data problems were reported in approximately half of more than 800 articles published in three leading personality journals. In these articles, unit nonresponse, attrition, and planned missingness were distinguished but missing item scores in trait measurement were reported most frequently. Listwise deletion was the most frequently used method for handling all missing-data problems. Listwise deletion is known to reduce the accuracy of parameter estimates and the power of statistical tests and often to produce biased statistical analysis results. This study proposes a simple alternative method for handling missing item scores, known as two-way imputation, which leaves the sample size intact and has been shown to produce almost unbiased results based on multi-item questionnaire data.

OBJECTIVE In order to broaden the view beyond posttraumatic stress disorder (PTSD) in children, we examined to what extent posttraumatic stress reactions, posttraumatic growth, and quality of life were related to each other and to traumatic exposure in the general population. METHOD 1770 children of 36 randomly selected primary schools (mean age = 10.24 years, 50% boys) reported in October/November 2006 on their worst experience (traumatic exposure was considered present when the described event fulfilled the A1 criterion for PTSD of the DSM-IV-TR) and filled out the Children's Responses to Trauma Inventory, the Posttraumatic Growth Inventory for Children, and the KIDSCREEN-27. Correlational and hierarchical linear regression analyses were carried out in a multiple imputation format. RESULTS Posttraumatic stress reactions were strongly related to posttraumatic growth (r = 0.41, p < .01) and quality of life (r = -0.47, p < .01). The latter 2 variables were weakly related; positively when controlling for posttraumatic stress reactions (r = 0.09, p < .01), negatively when not (r = -0.12, p < .01). Children who were exposed to trauma reported more posttraumatic stress reactions (β = .12, p < .01), more posttraumatic growth (β = .09, p < .01), and less quality of life (β = -.08, p < .01) than nonexposed children (effect sizes were small). CONCLUSIONS Negative and positive psychological sequelae of trauma can coexist in children, and extend to broader areas of life than specific symptoms only. Clinicians should look further than PTSD alone and pay attention to the broad range of posttraumatic stress reactions that children show, their experience of posttraumatic growth, and their quality of life.

In order to direct efforts to prevent children from being exposed to trauma and its psychosocial consequences, more knowledge is needed about which children are at risk. Therefore, we examined demographic risk factors for trauma exposure in a sample of Dutch primary school children in the general population (N = 1,770, mean age 10.24 years). Fourteen percent of the children reported exposure. Age was positively associated with exposure while sex, ethnicity, and region of residence did not emerge as significant risk factors. These results imply that prevention measures should be provided not only to groups of children who are traditionally considered vulnerable but broadly.

We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To obtain imputations reflecting uncertainty about the unknown model parameters, we use a nonparametric bootstrap procedure as an alternative to the more common full Bayesian approach. The proposed multiple imputation method, which is implemented in Latent GOLD software for latent class analysis, is illustrated with two examples. In a simulated data example, we compare the new method to well-established methods such as maximum likelihood estimation with incomplete data and multiple imputation using a saturated log-linear model. This example shows that the proposed method yields unbiased parameter estimates and standard errors. The second example concerns an application using a typical social sciences data set. It contains 79 variables that are all included in the imputation model. The proposed method is especially useful for such large data sets because standard methods for dealing with missing data in categorical variables break down when the number of variables is so large.

In this paper we test the hypothesis that beliefs about the ideal mother are convergent across cultures and that these beliefs overlap considerably with attachment theory’s notion of the sensitive mother. In a sample including 26 cultural groups from 15 countries around the globe, 751 mothers sorted the Maternal Behavior Q-Set to reflect their ideas about the ideal mother. The results show strong convergence between maternal beliefs about the ideal mother and attachment theory’s description of the sensitive mother across groups. Cultural group membership significantly predicted variations in maternal sensitivity belief scores, but this effect was substantially accounted for by group variations in socio-demographic factors. Mothers living in rural versus urban areas, with a low family income, and with more children were less likely to describe the ideal mother as highly sensitive. Cultural group membership did remain a significant predictor of variations in maternal sensitivity belief scores above and beyond socio-demographic predictors. The findings are discussed in terms of the universal and culture-specific aspects of the sensitivity construct., info:eu-repo/semantics/publishedVersion

Multiple imputation under a two-way model with error is a simple and effective method that has been used to handle missing item scores in unidimensional test and questionnaire data. Extensions of this method to multidimensional data are proposed. A simulation study is used to investigate whether these extensions produce biased estimates of important statistics in multidimensional data, and to compare them with lower benchmark listwise deletion, two-way with error and multivariate normal imputation. The new methods produce smaller bias in several psychometrically interesting statistics than the existing methods of two-way with error and multivariate normal imputation. One of these new methods clearly is preferable for handling missing item scores in multidimensional test data.

The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha, Loevinger's scalability coefficient H, and the item cluster solution from Mokken scale analysis of the complete data were compared with the corresponding results based on the data including imputed scores. The multiple-imputation methods, two-way with normally distributed errors, corrected item-mean substitution with normally distributed errors, and response function, produced discrepancies in Cronbach's coefficient alpha, Loevinger's coefficient H, and the cluster solution from Mokken scale analysis, that were smaller than the discrepancies in upper benchmark multivariate normal imputation.

Introduction: Infections with carbapenem-resistant Gram-negative bacteria (CRGNB) are increasing and are associated with a high mortality. Synergistic effects of combination therapy with a polymyxin, carbapenem, and rifampin have been observed in in vitro studies. Clinical data are limited to retrospective studies.Methods: We performed an observational cohort study of patients over 18 y of age who were treated with polymyxin B combination therapy.Results: One hundred and four patients were studied. The mean age was 77 y; 73% had recently received antibiotics, 67% had recently been hospitalized, and 47% lived in a nursing facility. The most common infections were pneumonia and urinary tract infection due to Acinetobacter baumannii (33%), Klebsiella pneumoniae (24%), and Pseudomonas aeruginosa (11%). Treatment regimens included polymyxin B with a carbapenem in 48%, with additional rifampin in 23%. Clinical success was achieved in 50% and reinfection occurred in 25%. Treatment-related acute renal failure occurred in 14.4%. No treatment-related hemodialysis was needed. All-cause hospital mortality was 47% and mortality after 6 months was 77%. No significant difference was found between treatment regimens. Age (odds ratio (OR) 10.4 per 10 y, p = 0.04), severity of acute illness (OR 2.2 per point, p < 0.001), and Charlson score (OR 1.12 per point, p = 0.04) were associated with hospital mortality. K. pneumoniae was associated with increased hospital survival compared to other CRGNB (p = 0.03).Conclusion: CRGNB infections are associated with previous antibiotic and health care exposure. Mortality is related to age and the severity of chronic and acute illness.

We examined associations of disorganized attachment and maternal depressive symptoms with infant autonomic functioning in 450 infant-mother dyads enrolled in the Generation R study. Maternal depressive symptoms were measured 2 months postpartum with the Brief Symptom Inventory. At 14 months, we assessed infant attachment with a slightly shortened Strange Situation and measured infant resting heart rate. Respiratory sinus arrhythmia (RSA) was calculated using spectral analysis. Higher levels of maternal postnatal depressive symptoms predicted lower resting RSA in disorganized infants (B = -0.31, SE = 0.15, p = .04, R(2) = .05) but not in nondisorganized infants (B = 0.05, SE = 0.06, p = .36). This effect was buffered in disorganized infants with a secondary secure attachment classification. Disorganized infants were more vulnerable to the effect of maternal postnatal depressive symptoms on the physiological stress systems.

Earlier research has shown that bootstrap confidence intervals from principal component loadings give a good coverage of the population loadings. However, this only applies to complete data. When data are incomplete, missing data have to be handled before analysing the data. Multiple imputation may be used for this purpose. The question is how bootstrap confidence intervals for principal component loadings should be corrected for multiply imputed data. In this paper, several solutions are proposed. Simulations show that the proposed corrections for multiply imputed data give a good coverage of the population loadings in various situations.

We evaluated the cost-effectiveness of HPV16/18 vaccination for girls aged 12 years in The Netherlands in addition to cervical cancer screening. For this purpose, we developed a simulation model that describes the relation between each of the high-risk human papillomavirus (hrHPV) types and cervical disease, allowing the occurrence of multiple type-specific infections. Model parameters were derived from Dutch cohort studies, including a large population-based screening trial, and from the national cervical cancer registry. The model satisfactorily reproduced Dutch data on HPV infection and the presence of cervical lesions. For our base-case scenario in which 85% of the girls aged 12 years were vaccinated against types 16/18 (95% efficacy, lifelong protection), the model predicted a decrease of 60% in the number of cervical cancer cases and cervical cancer deaths indicating that substantial health benefits can be achieved. Health savings were robust against changes in the vaccine efficacy (varied from 85% to 98%) but savings showed a substantial reduction when the efficacy started waning 10 years after vaccination. The discounted costs per quality-adjusted life year (QALY) were € 19,500/QALY (range € 11,000 to € 25,000/QALY) and lied near the cost-effectiveness threshold of € 20,000/QALY used in The Netherlands. The simulations further showed that vaccination cannot replace screening because vaccination without screening was less effective than screening in preventing cancer in women over 40 years of age. In conclusion, our model results support the implementation of HPV16/18 vaccination in young women in addition to cervical cancer screening. © 2008 Wiley-Liss, Inc.

In 1993 Albach investigated the long-term consequences of sexual abuse on psychological health. A group of abused women and a control group of non-abused women were asked to fill in a questionnaire assessing posttraumatic stress disorder (PTSD). For ethical reasons, the abused women were warned that filling in the questionnaire might be emotionally stressful. The control group did not receive this warning. The abused women scored higher on the questionnaire than the non-abused women. The warning they received may have influenced their reports. Our experiment investigated this 101 psychology students were divided into two groups, one who received a warning and a control group who did not. The hypothesis was that people who had been previously warned would score higher on a PTSD questionnaire than people who had not. There were, however, no significant differences in mean PTSD scores and no known initial differences between groups.