Primary school teacher outcomes from online professional development for physical literacy: A randomised controlled trial

IntroductionPrimary (or elementary) school teachers are often relied upon to provide children with opportunities for physical literacy development; however, many of these teachers feel they lack the skills to effectively promote or ‘teach’ physical literacy. We examined the effects of an online physical literacy professional development program—relative to receiving widely-available online resources or continuing with ‘normal’ practice—on primary school teachers’ physical literacy knowledge and application.MethodsA parallel three-arm randomised controlled trial in which 92 primary school teachers were assigned to (a) an online professional development program designed to support teachers’ physical literacy instructional skills (‘intervention’), (b) receive widely-available online physical literacy resources (‘comparison’), or (c) a typical practice ‘control’ condition. Data were collected at baseline and following the four-week intervention period. The primary outcome was teachers’ physical literacy knowledge and application; secondary outcomes included teachers’ self-reported perceptions of confidence, values, and barriers. Between-group differences were assessed using the intention-to-treat principle with analysis of covariance accounting for demographic factors and baseline differences in the focal outcome.ResultsTeachers in the intervention arm scored significantly higher on post-intervention physical literacy knowledge and application than their counterparts in both the control (d = .47, p = .044) and comparison (d = 0.87, p = .007) arms. This pattern of differences was also observed for teachers’ value of physical literacy, autonomy support, and perceived personal barriers.ConclusionTo the best of our knowledge, this is the first adequately powered randomised controlled trial to demonstrate improvements in physical literacy instructional outcomes as a result of primary school teacher participation in targeted online professional development.     

新世纪20年国内测验信度研究

随着验证性因子分析模型的应用, 信度研究进入了崭新的发展阶段。新世纪前20年国内有关测验信度的研究有三条发展主线。一是基于验证性因子模型的信度发展, 包括同质性系数、合成信度、最大信度等; 二是数据类型的拓展, 包括两水平和追踪数据的信度; 三是信度用途的拓展, 如评分者信度、编码者信度等。对于通常的测验(题目之间的测量误差不相关), 如果α系数够高, 信度就够高; 否则使用合成信度。如果一个统计模型中所有变量的合成信度都很高(超过0.95), 使用显变量建模与使用潜变量建模的结果差别不大; 否则, 使用潜变量建模较好。     

Demonstrating and eliminating the aggregation effect in physical skill tasks

Confidence judgments can be elicited in multiple ways. One of these procedures is to provide confidence judgments regarding each of a number of cases (individual judgments). A second procedure is to provide confidence judgments about a set of items (an aggregate judgment). Much research has demonstrated an aggregation effect—that individual judgments are more confident than aggregate judgments—within the cognitive knowledge domain. However, this effect has not previously been investigated with physical performance skill tasks. In three experiments, participants gave individual and aggregate judgments regarding the number of successful tosses they would make in either a ring toss, ping‐pong toss, or basketball toss task. In keeping with the aggregation effect, individual judgments were more confident than were aggregate judgments of success. Additionally, we eliminated the aggregation effect in Experiments 2 and 3 by employing a case‐specific base rate manipulation. Consistent with previous research with cognitive tasks, these results suggest that individual confidence judgments for physical skill tasks are determined primarily by characteristics associated with the individual case to be judged, whereas aggregate confidence judgments are determined by a more general evaluation of one's ability in the domain. Copyright © 2009 John Wiley & Sons, Ltd.     

Percentile Norms and Accompanying Interval Estimates from an Australian General Adult Population Sample for Self‐Report Mood Scales (BAI,BDI, CRSD,CES‐D,DASS, DASS‐21, STAI‐X,STAI‐Y,SRDS, and SRAS)

Despite their widespread use, many self‐report mood scales have very limited normative data. To rectify this, Crawford et al. have recently provided percentile norms for a series of self‐report scales. The present study aimed to extend the work of Crawford et al. by providing percentile norms for additional mood scales based on samples drawn from the general Australian adult population. Participants completed a series of self‐report mood scales. The resultant normative data were incorporated into a computer programme that provides point and interval estimates of the percentile ranks corresponding to raw scores for each of the scales. The programme can be used to obtain point and interval estimates of the percentile ranks of an individual's raw scores on the Beck Anxiety Inventory, the Beck Depression Inventory, the Carroll Rating Scale for Depression, the Centre for Epidemiological Studies Rating Scale for Depression, the Depression, Anxiety, and Stress Scales (DASS), the short‐form version of the DASS (DASS‐21), the Self‐rating Scale for Anxiety, the Self‐rating Scale for Depression, the State–Trait Anxiety Inventory (STAI), form X, and the STAI, form Y, based on normative sample sizes ranging from 497 to 769. The interval estimates can be obtained using either classical or Bayesian methods as preferred. The programme (which can be downloaded at http://www.abdn.ac.uk/~psy086/dept/MoodScore_Aus.htm ) provides a convenient and reliable means of obtaining the percentile ranks of individuals' raw scores on self‐report mood scales.     

The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes,confidence intervals,and meta‐analysis

Estimation based on effect sizes, confidence intervals, and meta‐analysis usually provides a more informative analysis of empirical results than does statistical significance testing, which has long been the conventional choice in psychology. The sixth edition of the American Psychological Association Publication Manual now recommends that psychologists should, wherever possible, use estimation and base their interpretation of research results on point and interval estimates. We outline the Manual's recommendations and suggest how they can be put into practice: adopt an estimation framework, starting with the formulation of research aims as ‘How much?’ or ‘To what extent?’ questions. Calculate from your data effect size estimates and confidence intervals to answer those questions, then interpret. Wherever appropriate, use meta‐analysis to integrate evidence over studies. The Manual's recommendations can assist psychologists improve they way they do their statistics and help build a more quantitative and cumulative discipline.     

COMPARING PLEASURE AND PAIN: THE FUNDAMENTAL MATHEMATICAL EQUIVALENCE OF REWARD GAIN AND SHOCK REDUCTION UNDER VARIABLE INTERVAL SCHEDULES

The relationship between positive and negative reinforcement and the symmetry of Thorndike's law of effect are unresolved issues in operant psychology. Here we show that, for a given pattern of responding on variable interval (VI) schedules with the same programmed rate of food rewards (positive reinforcement VI) or electric shocks (negative reinforcement VI), there is a fundamental mathematical equivalence between reward gain and shock reduction. We also provide the first normative account of how animals should respond on a negative VI schedule, showing that it is better to space responses evenly than to respond with a variable interresponse time (IRT). Published data from rats, however, indicate that these animals respond irregularly, often with a burst of activity immediately following a shock. While this is irrational in the experimental setting, it may represent an appropriate response to the heterogeneity of stimuli commonly encountered in natural environments. We discuss the broader implications of our analysis for understanding how animals evaluate appetitive and aversive stimuli.     

Testing the Physical Activity Self-Definition Model among low-active adults participating in a physical activity intervention

Seeing oneself as a physically active person is one of the strongest predictors of physical activity behaviour and self-regulatory strategies. Determining whether and how physical activity self-perceptions can be stimulated may help low-active individuals who do not see themselves as a physically active person become more active. Cross-sectional research has tested the Physical Activity Self-Definition (PASD) model among active samples; longitudinal studies among low-active adults have yet to be done. The purpose of this study was to test the predictive power of the PASD model among low-active adults over a 16-week physical activity intervention. Participants completed surveys of validated questionnaires in-person at baseline (pre-intervention) and at 16-weeks (end of intervention) at one of two primary care facilities. The final sample included 119 low-active adults. Partial least squares-structural equation modeling indicated that the original model had small-medium predictive power (Q² = 0.22; SRMR = 0.13 [0.05, 0.07]; RMSE = 1.13; MAE = 0.9; BIC = 1348.40). Two paths were added in the revised model (perceived wanting—PASD; perceived ability—perceived commitment), which explained an additional 4% and 5% of the variance in perceived commitment (R² = 0.62 [0.48, 0.72]) and PASD (R² = 0.74 [0.64, 0.80]; all p’s < 0.001), respectively. The revised model had medium predictive power (Q² = 0.25; SRMR = 0.11 [0.05, 0.06]; RMSE = 1.1; MAE = 0.87; BIC = 1332.84) All path coefficients remained positive and significant at p ≤ .001. Among low-active adults, perceived wanting and perceived ability may be more salient when engaging in physical activity and regarding themselves as a physically active person. Findings may support practitioners and health care professionals in designing physical activity interventions to foster PASD among low-active adult populations.     

Decision‐makers: Introductory paper

Training, whether of marginalized young people or of those fortunate enough to be better integrated, must take account of the importance and the needs of the enterprise. The young must be given motivation, by giving meaning to the activity of the enterprise.     

Back to the basics: Rethinking partial correlation network methodology

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ₁-based regularization. The glasso was developed and optimized for high-dimensional settings where the number of variables (p) exceeds the number of observations (n), which is uncommon in psychological applications. Here we propose to go ‘back to the basics’, wherein the precision matrix is first estimated with non-regularized maximum likelihood and then Fisher Z transformed confidence intervals are used to determine non-zero relationships. We first show the exact correspondence between the confidence level and specificity, which is due to 1 minus specificity denoting the false positive rate (i.e., α). With simulations in low-dimensional settings (p ≪ n), we then demonstrate superior performance compared to the glasso for detecting the non-zero effects. Further, our results indicate that the glasso is inconsistent for the purpose of model selection and does not control the false discovery rate, whereas the proposed method converges on the true model and directly controls error rates. We end by discussing implications for estimating GGMs in psychology.     

Post choice information integration as a causal determinant of confidence: Novel data and a computational account

Confidence judgments are pivotal in the performance of daily tasks and in many domains of scientific research including the behavioral sciences, psychology and neuroscience. Positive resolution i.e., the positive correlation between choice-correctness and choice-confidence is a critical property of confidence judgments, which justifies their ubiquity. In the current paper, we study the mechanism underlying confidence judgments and their resolution by investigating the source of the inputs for the confidence-calculation. We focus on the intriguing debate between two families of confidence theories. According to single stage theories, confidence is based on the same information that underlies the decision (or on some other aspect of the decision process), whereas according to dual stage theories, confidence is affected by novel information that is collected after the decision was made. In three experiments, we support the case for dual stage theories by showing that post-choice perceptual availability manipulations exert a causal effect on confidence-resolution in the decision followed by confidence paradigm. These finding establish the role of RT2, the duration of the post-choice information-integration stage, as a prime dependent variable that theories of confidence should account for. We then present a novel list of robust empirical patterns (‘hurdles’) involving RT2 to guide further theorizing about confidence judgments. Finally, we present a unified computational dual stage model for choice, confidence and their latencies namely, the collapsing confidence boundary model (CCB). According to CCB, a diffusion-process choice is followed by a second evidence-integration stage towards a stochastic collapsing confidence boundary. Despite its simplicity, CCB clears the entire list of hurdles.