Data capacity: 9,999,999 persons by 60,000 items with rating scales up to 32767 categories for each item.Windows: Winsteps is 64-bit/32-bit software. It runs successfully underWindows 11, 10, 8, 7, Vista, but not Windows XP, 2000, ME, NT, 98, 95, 3.1.For Windows XP and 32-bit-only Windows installations, Winsteps 4.8.2 is available. Purchase a standard Winsteps license, then request the earlier Winsteps version.Mac, Linux,Chrome OS: Winsteps runs under Windows installed on the target hardware using special-purpose software.Please check compatibility by downloading and running the free student/evaluation/demo version, MINISTEP- same as Winsteps but limited to 25 items and 75 persons/cases.
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Winsteps constructs Rasch measures from simple rectangular datasets, usually of persons and items, using JMLE and CMLE. Users report that, after initial familiarization,it is straightforward to use in combination with other software.Item types that can be combined in one analysis includedichotomous, multiple-choice, and multiple rating-scale and partialcredit items. Paired comparisons and rank-order data can also beanalyzed. Missing data is no problem. Winsteps is designed as atool that facilitates exploration and communication. The structureof the items and persons can be examined in depth. Unexpected datapoints are identified and reported in numerous ways. Powerfuldiagnosis of multidimensionality through principal componentsanalysis of residuals detects and quantifies substructures in thedata. The working of rating scales can be examined thoroughly, andrating scales can be recoded and items regrouped to share ratingscales as desired. Measures can be fixed (anchored) at pre-setvalues. Winsteps is intended for practitioners who must makepractical and quick decisions along the path to constructingeffective tests, and who must then communicate their resultsusefully to end users. The developers of Winsteps use the programdaily in their own work, and are continually adding new features asa result of their own experience and feedback from users. Typicalapplications include educational tests, psychological assessments,attitude surveys, patient performance protocols, and calibratingadaptive-test item banks. Winsteps can process up to 9,999,999persons, 60,000 items, and each item can have a rating, grouped-rating or partial-credit scale of upto 32,767 categories. Training courses are held regularly.Student/evaluation/demo versions of Winsteps, called Ministep, can be downloaded free from www.winsteps.com/ministep.htm.
Everything by downloadSoftware download link sent by emailUser Guide (PDF)One year of free updates by downloadPurchase on-line by credit cardFacets $149.Winsteps $149. Burn your own back-up CD-ROM or add for back-up CD-ROMuses original installation password+ $10. (or so)optional CD-ROMWire transfers, etc.+ Additional fees apply.Multi-user site license detailsPersonal-circle: $499.Institution-wide: $2,250.If desired: Print your own User Manual from free PDF files: (also included in software downloads)download Winsteps PDF file nowdownload Facets PDF file nowDownload free student/evaluation versions, MINISTEP and MINIFAC, to check out software features.Free update period expired?Download current software& further updates free by download for two years$49Please email Mike Linacre, author of Winsteps: mike \at/ winsteps.com to obtain renewal/update procedureLink to full End-User License AgreementState-of-the-art : single-user and site licenses : free student/evaluation versions : download immediately : instructional PDFs : user forum : assistance by email : bugs fixed fast : free update eligibility : backwards compatible : money back if not satisfied Rasch, Winsteps, Facets online Tutorials
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All outcomes were associated with perceived socioeconomic status, suggesting that perceived low socioeconomic status should be addressed as a serious risk factor and included as a moderator in similar trials.
The Healthy Learning Mind (HLM) -study is a carefully controlled, registered trial that evaluates the short and long term (6 and 12 months) effects of a mindfulness intervention (Stop and be) (Huppert & Johnson, 2010) compared to an active control program (relaxation) and non-treatment group. Mindfulness interventions with children and adolescents have shown improvement in cognitive capabilities, such as attention and emotion regulation, as well as psychological measures of stress, coping and resilience (Kuyken et al., 2013; Mak, Whittingham, Cunnington, & Boyd, 2018; Vickery & Dorjee, 2015).
Our results related to the baseline characteristics indicate that measuring perceived SES should be routinely included in similar trials. Subjective measures enable individuals to evaluate the experience of their own financial situation by both cognitive and affective reactions (Diener et al., 1985). They are also better equipped to examine non-financial effects (e.g. societal attitudes towards wealth). Living community and peer group can also affect how individuals perceive their situation compared to others (Dolan, Peasgood, & White, 2008). Despite previous findings, indicating that affluence in general does not seem to affect sleep patterns (e.g. Gariepy et al., 2020), our results suggest that for children and adolescents they may be related. These results are perhaps not surprising but underline the importance of measuring financial wellbeing and how it is experienced in families, as well as considering the burden to comprehensive student health outcomes. As observed earlier (Arber et al., 2014), perceived financial wellbeing mediates the income related health outcomes for adults and may have similar pathways in adolescence. Family financial stress can have many effects, some mediated and some direct, but still acutely experienced by all family members (Ponnet, 2014).
As we found the most pronounced cross-correlations for health-related quality of life (KINDL-R) together with socioemotional functioning (SDQ) and depressive symptoms (RBDI), we could argue that these questionnaires address the variable of well-being from different perspectives but are highly related. Health related quality of life seems to be competent in capturing some common elements of wellbeing and we suggest it as a comprehensive measure that can give useful information in similar trials. Correlations are in general stronger for girls compared to boys, stay quite stable across grades and are stronger for Health-related quality of life compared with other examined variables. Interestingly, self-kindness (C/SK) does not seem to have a strong relation with mindfulness (CAMM), which may indicate that this aspect of wellbeing is not directly related with dispositional mindfulness and may require practice.
The HLM-study has been one of the first large-scale cluster randomized controlled trials (RCT) examining school-based mindfulness interventions. The baseline data with high response rate suggest that the participants are representative of children and adolescents in Finland and results largely reflect the existing research. Comparison to other national data is partly difficult due to varying age groups and samples, but our findings are mostly in line with population studies elsewhere. We also suggest that moderating effects of gender, age and perceived socioeconomic status should be considered in similar trials. Moreover, our data showed that girls had more depressive symptoms than boys and lower scores in mindfulness and self-kindness. A larger part of the adolescent sample suffered from moderate and severe depression than from mild depression, contrary to previous findings. Further results showed that girls have lower quality of life scores than boys and quality of life is somewhat lower compared to European data. For all outcomes, the gender differentiation was apparent, and girls were consistently worse off in terms of wellbeing. As expected, older adolescents experience more disruption in their wellbeing, and results were again consistent across outcomes. Perceived socioeconomic status is associated with the results across health and wellbeing outcomes, i.e. higher the financial wellbeing, higher the wellbeing overall. These findings indicate that when considering the potential risk factors to adolescent health, self-reported socioeconomic inequalities should be addressed.
Alternatively, it may be that between-group differences in outcomes, or greater magnitude of changes in outcomes, would have been observed had we supplemented the online programs with more frequent human contact or therapist guidance. The current trial intentionally evaluated the online intervention with minimal provider contact to facilitate future reproducibility in routine practice settings and in fact used 75% less frequent human (phone) contact than was used in a pilot test of the intervention (Stinson et al., 2010). It may be that supplementing the online self-management intervention with regular contact with a pediatric psychologist or other provider would result in greater adherence, larger treatment effects, and/or a greater distinction in outcomes from only accessing general educational information about JIA online. This remains speculative given the wide variability in the amount and means of provider contact among Internet intervention studies (Vigerland et al., 2016), lack of consistent reporting and definitions of adherence for these types of studies, and no known studies of Internet interventions for pediatric conditions that systematically vary therapist contact. One study with JIA patients did demonstrate improved self-efficacy, a variable found in the current study to predict amount of change in pain and HRQOL, from weekly video calls with peer (young adult) mentors (Stinson et al., 2016); combining a peer video call program with the online resources evaluated in the current study may be a novel way to enhance treatment effects. 2ff7e9595c