Physical activity levels and social interactions of preschoolers with and without disabilities.

This study examined differences of physical activity levels and social interactions among preschoolers with/without disabilities and those at risk for a developmental delay (DD). Sixty preschoolers (20 in each group) were film during their designated large gross motor time. Their physical activity levels and social interaction were examined using the physical activity level screening. Regarding physical activity levels, the result of a two-way analysis of variance indicated significant main effects for groups, F(2,54)=4.192, P=0.02; preschoolers at risk for a DD were more active than preschoolers without disabilities, P=0.02. For social interaction, a significant main effect for groups, F(2,54)=8.994, P<0.001 was found; Preschoolers at risk for a DD were more interactive than preschoolers with/without disabilities, P=0.001 and P=0.002, respectively. There were no significant gender differences across three groups on physical activity levels and social interactions. Although preschoolers at risk for a DD was more active and interactive than the other groups, preschoolers across three groups engaged in light physical activity levels, and had limited social interactions with peers.

Validity and reliability evidence of the physical activity level screening for preschoolers with developmental delays and/or disabilities.

Psychometric properties of the physical activity level screening (PALS) for preschoolers with a developmental delay and/or a disability were examined in this study. The mean age of participants (16 boys and 14 girls) was 54.9 months (standard deviation, 10.4). Each participant wore one Actical accelerometer and was filmed for 20 min during the active play time. Intrarater and interrater reliability, and validity of the PALS data were examined. The proportion of agreement for intrarater was 87.5% and 85.6% for interrater reliability. Intraclass correlation coefficients were 0.99 for intrarater and 0.96 for interrater. The PALS data showed moderate correlation with Actical data (r=0.67, P<0.01).

Interpreting linked psychomotor performance scores.

Given that equating/linking applications are now appearing in kinesiology literature, this article provides an overview of the different types of linked test scores: equated, concordant, and predicted. It also addresses the different types of evidence required to determine whether the scores from two different field tests (measuring the same construct) can be used interchangeably (equated). Thus, evidence issues are addressed to help the reader determine whether the creation of equipercentile equated or concordant scores is appropriate and useful. The article is organized according to the following issues: (a) the degree to which the two tests measure the same construct, (b) the equating/linking process, (c) the evaluation of equating/ linking function, and (d) stability of equating/linking function across populations.

Evaluating judge performance in sport.

Many sports, such as, gymnastics, diving, ski jumping, and figure skating, use judges' scores to determine the winner of a competition. These judges use some type of rating scale when judging performances (e.g., figure skating: 0.0 - 6.0). Sport governing bodies have the responsibility of setting and enforcing quality control parameters for judge performance. Given the judging scandals in figure skating at the 1998 and 2002 Olympics, judge performance in sport is receiving greater scrutiny. The purpose of this article is to illustrate how results from Rasch analyses can be used to provide in-depth feedback to judges about their scoring patterns. Nine judges' scores for 20 pairs of figure skaters who competed at the 2002 Winter Olympics were analyzed using a four-faceted (skater pair ability, skating aspect difficulty, program difficulty, and judge severity) Rasch rating scale model that was not common to all judges. Fit statistics, the logical ordering of skating aspects, skating programs, and separation indices all indicated a good fit of the data to the model. The type of feedback that can be given to judges about their scoring pattern was illustrated for one judge (USA) whose performance was flagged as being unpredictable. Feedback included a detailed description of how the rating scale was used; for example, 10% of all marks given by the American judge were unexpected by the model (Z > |2|). Three figures illustrated differences between the judge's observed and expected marks arranged according to the pairs' skating order and final placement in the competition. Scores which may represent "nationalistic bias" or a skating order influence were flagged by looking at these figures. If sport governing bodies wish to improve the performance of their judges, they need to employ methods that monitor the internal consistency of each judge as a many-facet Rasch analysis does.

Aerobic exercise equipment preferences among older adults: a preliminary investigation.

The purpose of this study was to develop an instrument that would measure the aerobic exercise equipment preferences of a frail older population and to see, despite a small sample size, if a many-facet Rasch analysis would provide useful information concerning the construct validity of the instrument and the equipment preferences of the sample. Sixteen ambulatory seniors (M = 82.0 yr +/- 6.6; 4 males and 12 females), who resided in a local retirement community and were involved in a structured fitness program, evaluated the following exercise equipment: Schwinn Air-Dyne, Nu-Step Recumbent Stepper; Monark bicycle ergometer; Stairmaster; and PTS Turbo Recumbent Bicycle. Participants used the equipment for 5 min. and then completed the survey via a structured interview technique. Test-retest reliability coefficients indicated the participants' responses were stable across days for each piece of exercise equipment (proportions of agreement >.83; km >.77). A many-facet (equipment, items, participants) Rasch analysis verified that 12 closed format items defined a linear construct of equipment preference (separation = 1.8; reliability =.77). The pieces of equipment were placed on the linear continuum according to their equipment preference measures (separation = 3.21; reliability =.91) derived from the participants' response patterns to the items (separation = 1.43; reliability =.67). Although the MNSQ infit and outfit statistics were acceptable for each facet, the items did not target the equipment very well. Suggested changes to the instrument include converting questions to statements to use with Likert response categories; converting negative wording to positive phrasing, and adding items related to seat comfort, foot pedal placement, and visibility of display panel. The Nu-Step and Schwinn were the most preferred pieces of equipment while the Stairmaster was the least preferred. This preliminary investigation illustrates how useful information can be obtained from a many-facet Rasch analysis to guide instrument revision and better understand exercise equipment preferences among a frail older population.