Refining the Canadian Assessment of Physical Literacy based on theory and factor analyses

The CAPL was developed to meet the demand for an assessment tool that could be used to produce valid and reliable scores that were representative of children’s progress on their physical literacy journey [9]. The creation of the CAPL involved consultation with practitioners (e.g., physical education teachers) and researchers, an extensive review of Canadian school physical education curricula, the identification of existing assessments, and the creation of novel assessments when no others existed (see [10] for additional details). The feasibility of the CAPL was examined through an iterative process to ensure that children could perform the assessment protocols, the time to administer the protocols was reasonable, and the personnel needed to implement the protocols was appropriate [10].

Currently, the CAPL is comprised of 25 indicators chosen to align with the internationally accepted and recently published Canadian Consensus Statement definition of physical literacy [5, 6]. Nonetheless, CAPL creators acknowledge competing definitions of physical literacy [4]. Despite the various definitions of physical literacy, it is important to note that the CAPL is anchored to the Canadian Consensus Statement definition promoted by sector leaders [5, 11] to include four essential and interrelated elements proposed by other researchers [2]: motivation and confidence (affective); engagement in physical activities for life (behavioural); knowledge and understanding (cognitive); and physical competence (physical). Each indicator can be used alone or in combination, depending on whether the goal of the assessment is an overall measure of physical literacy or a more focused evaluation of one domain or aspect. The four interrelated domains of the CAPL for children aged 8–12 years are: (1) Physical Competence; (2) Daily Behaviour; (3) Motivation and Confidence; and (4) Knowledge and Understanding.

With recognition that validation is an ongoing process [12], the CAPL has undergone extensive modifications to reflect advances in physical literacy theory and to account for results from investigations of score reliability and validity. Although many indicators of the CAPL domains have remained consistent across time, some indicators of each domain have been modified, removed, or added based on scientific evidence and theoretical considerations. For instance, the curl-up and push-up protocols originally envisioned as part of the CAPL were removed and replaced with the static plank hold [13]. Additionally, the initial movement skill assessment was modified to clarify/simplify scoring and for use in smaller spaces [14]. Subsequently, investigation of the factor structure along with feedback from experts through a Delphi process was undertaken to further validate and weight the CAPL scores [8].

In its original conceptualization, the CAPL was comprised of four domains labelled (1) fundamental motor skills; (2) physical activity behaviour; (3) physical fitness; and (4) knowledge, awareness, and understanding (see Fig. 1a) [8, 15]. In an effort to determine the configuration and relative importance to physical literacy, a 3-stage Delphi process was undertaken wherein 19 experts in childhood physical activity and fitness evaluated the original CAPL model [8]. Results from the Delphi process indicated that the CAPL model should be reflected as three inter-related domains labelled (1) Physical Competence (subsuming measurements of motor skill and physical fitness); (2) Motivation and Confidence; and (3) Knowledge and Understanding, which together are encompassed by a fourth domain labelled (4) Daily Physical Activity Behaviour (see Fig. 1b).

At this juncture, it is important to acknowledge the debate within the physical literacy literature around the inclusion of physical fitness and physical activity as indicators of physical literacy [2, 4, 16]. Notably, indicators of physical fitness were included within CAPL given that Whitehead [16] has argued that physical literacy is a journey that requires the capacity to be physically competent. CAPL developers have argued that the measurement of fitness can be used to indicate an individual’s capacity to sustain physical activity for life [17]. Similarly, the creators of CAPL interpret the Canadian Consensus Statement definition to mean that when people value and take responsibility for engaging in physical activity, they will demonstrate this by being physically active. From this perspective, the creators of CAPL believe the definition of physical literacy includes physical activity behaviour as part of physical literacy. This perspective is consistent with the model proposed by Robinson and Randall [2] and the elements of physical literacy outlined in the Canadian Consensus Statement [5, 6]. Corroborating these arguments to include physical fitness and physical activity, the Delphi experts also recommended the inclusion of these two objectively assessed domains of physical literacy (i.e., Physical Competence and Daily Behaviour) and recommended that they receive more relative weight for creating an overall physical literacy score compared to the subjective domains (i.e., Motivation and Confidence, and Knowledge and Understanding). Finally, Francis and colleagues [8] recommended further inquiry into the factor structure of CAPL scores, with larger samples and factor analytic models to better demarcate the relative weighting of each CAPL domain in the overall composite score of physical literacy.

Following the recommendations from the Delphi process, data were collected with the intention of determining the relative importance of each CAPL domain and providing further validity evidence for the CAPL structure and scoring. To this end, Longmuir and colleagues [10] examined the factor structure of CAPL scores with a Canadian sample of 489 children. Results indicated that the CAPL scores should be reflected as four separate but inter-related domains of (1) Physical Competence, (2) Motivation and Confidence, (3) Knowledge and Understanding, and (4) Daily Behaviour (see Fig. 1c).

Despite the preliminary evidence, limitations of this past work were apparent. First, given the complexity of the full CAPL protocol and the modest sample size, not all indicators were included in the factor analytic model [10]. Second, rather than using each question from the Knowledge and Understanding questionnaire as indicators, one overall composite indicator was used. Finally, a careful inspection of the factor loadings obtained in the factor analysis called into question the salience of certain CAPL indicators for representing their respective domains. For example, standardized body mass index (BMI) z scores, total physical activity scores derived from pedometer step counts, and screen time scores were weak indicators (λs < 0.27) of their respective CAPL domains [10].

In the present study, using a much larger dataset, we re-examined validity evidence of scores from the full CAPL protocol based on factor structure and content representation alongside recent advances in the conceptualization of physical literacy, and relying on scientific and theoretical advances in physical literacy research. A second purpose was to reassess the relative weight of each domain for creating an overall physical literacy score. Our overall goal was to maximize content representation and validity evidence while reducing construct irrelevant variance and participant burden. The results from this study will set the stage for the development of CAPL-2 – a condensed and more theoretically aligned version of CAPL.

It is important to recognize that in this study we were not examining the validity of each indicator (e.g., grip strength) but, rather, examining how each indicator coalesces to demarcate a CAPL domain. Therefore, although we will present evidence for a reduced model of the CAPL, we do not wish to imply that the indicators removed were in some way invalid, but rather that they do not configure optimally, theoretically, statistically, or logistically in combination with the other indicators to create a particular CAPL domain. It is also important to note that initially we took a confirmatory approach to examine the a priori hypothesized model of CAPL based on past theory and empirical evidence [8, 9]. When the model did not fit the data, a more exploratory approach through the lens of confirmatory factor analysis was used; however, to avoid the negative consequences of data-driven specification searches [18], extensive discussion of substantive theory and alignment preceded data-driven modifications. Exploratory modifications were not made unless they were theoretically anchored and were supported by all co-authors. Further, all modifications were made with suggestions for cross-validation in future research.

A complete description of the participants and procedures for these data are presented by Tremblay and colleagues in this issue [19]. Briefly, Canadian children (n = 10,034; 50.1% girls) ranging in age from 8.0 to 12.9 (M_age = 10.6, SD = 1.2 years) completed CAPL testing. This project was approved by the Children’s Hospital of Eastern Ontario Research Ethics Board (Ottawa, Ontario; coordinating centre) and research ethics boards at each site. Parents or legal guardians provided written informed consent and children provided verbal assent.

The CAPL is comprised of standardized assessments with evidence of score reliability and validity in children aged 8 to 12 years [8, 14, 15, 20‐25]. A detailed description of each assessment protocol can be found at www.​capl-ecsfp.​ca.

Model 1 of Physical Competence was a poor fit (MLRχ²₍₁₄₎ = 7313.86, p < 0.001, CFI = 0.583, RMSEA = 0.231, 90% CI [0.226, 0.235]; see Table 1). Waist circumference had a problematic factor loading (λ = 0.99, p < 0.05). Because BMI z score and waist circumference are both indicators of body composition and are highly correlated (r = 0.80, p < 0.05), an error covariance between these two indicators was estimated in Model 2. The addition of the error covariance between BMI z score and waist circumference improved model fit, but model fit remained unacceptable (MLRχ²₍₁₃₎ = 4670.81, p < 0.001, CFI = 0.734, RMSEA = 0.191, 90% CI [0.187, 0.196], r_{BMIz.waist circumference} = 0.781, p < .001). Grip strength, sit and reach, BMI z score and waist circumference had low factor loadings (λs < 0.32). Conceptually, BMI z score and waist circumference might not be good indicators of physical competence as they might load better on a body composition factor. Additionally, grip strength and sit and reach may be weak indicators of physical competence in comparison to the other indicators because they do not reflect the natural movements children engage in during active pursuits. Therefore, grip strength, sit and reach, BMI z score, and waist circumference were removed. Model 3 was estimated with only PACER, CAMSA, and plank as indicators. Model 3 was just identified and, therefore, fit indices could not be obtained. Nonetheless, inspection of the factor loadings indicated that the PACER, plank, and CAMSA were moderate to strong indicators of physical competence (λs > 0.55; see Table 1). Model 3 was retained as the final model for physical competence.

All pedometer step count scores were divided by 100 to reduce the variance of this indicator and assist with model convergence. Model 1 was just identified and therefore fit statistics could not be obtained. Inspection of the factor loadings indicated that self-reported screen time had the lowest factor loading (λ = 0.35; see Table 2). Although screen time and sedentary behaviours are conceptualized under the movement continuum [32], we reasoned that screen time might not belong conceptually in a measurement model of physical literacy given recent evidence showing that physical activity and sedentary behaviour are separate and weakly correlated movements [33, 34]. Therefore, a decision was made to remove self-report screen time from the model. Finally, although the factor loading of pedometer step counts was relatively weak (λ = 0.40), pedometers are considered to be a more direct indicator of physical activity compared to self-report physical activity [35]. As such, it was retained in the model on conceptual and content representation grounds. Model 2, removing self-report screen time from daily behaviour, could not be estimated with only two indicators. Therefore, the factor loadings of the Daily Behaviour domain were further examined in the full measurement model.

Model 1 of the Motivation and Confidence domain provided an unacceptable fit to the data (MLRχ²₍₅₎ = 1529.61, p < 0.001, CFI = 0.876, RMSEA = 0.176, 90% CI [0.168, 0.183]; see Table 3). Modification indices (Modification Index = 1265.62) suggested that there was a large error covariance between the indicators of “activity compared to others” and “skill compared to others”. We reasoned that this error covariance could be attributable to the similar wording and response options of these questions. Model 2, with an error covariance between these two indicators, provided an excellent fit to the data (MLRχ²₍₄₎ = 365.42, p < 0.001, CFI = 0.971, RMSEA = 0.096, 90% CI [0.087, 0.104] r_{activity compared to others and skill compared to others} = 0.42, p < 0.01; see Table 3). Despite excellent model fit, we removed “activity compared to others”, for two reasons. First, the two items with the error covariance were very similar and might therefore cause redundancy in the model (i.e., each is not adding unique construct relevant variance). Second, the indicator “activity compared to others” had the lowest factor loading compared to the indicator “skill compared to others”. The model was re-estimated excluding “activity compared to others.” Model 3 for Motivation and Confidence provided an excellent fit to the data (MLRχ²₍₂₎ = 188.94, p < 0.001, CFI = 0.979, RMSEA = 0.097, 90% CI [0.086, 0.109]; see Table 3) and was retained as the final model.

At this point, a supplemental analysis was run to examine if specifying activity preferences as an indicator of Motivation and Confidence provided a good fit. In this analysis, Model 3 of motivation and confidence was re-estimated, including the categorical indicator of activity preferences and using the WLSMV estimator given the combination of categorical and continuous indicators. Compared to Model 3 of motivation and confidence, including the activity preference indicator degraded model fit (WLSMVχ²₍₅₎ = 509.66, p < 0.001, CFI = 0.936, RMSEA = 0.101, 90% CI [0.094, 0.109]). Although the indicator of activity preferences loaded relatively strongly on motivation and confidence (λ = 0.576, p < 0.01), a decision was made to not include this indicator because it decreased overall model fit of the domain and because it is a dichotomous indicator that may not provide as much content representation compared to the current indicators of motivation and confidence.

Model 1 with all original indicators of Knowledge and Understanding provided an unacceptable model fit (WLSMVχ²₍₃₅₎ = 627.92, p < 0.001, CFI = 0.881, RMSEA = 0.041, 90% CI [0.039, 0.044]; see Table 4). In Model 2, the indicators of safety, activity preferences, and screen time guidelines were removed because of their weak factor loadings (λs < 0.20; see Table 4). Model 2 provided an acceptable model fit (WLSMVχ²₍₁₄₎ = 211.43, p < 0.001, CFI = 0.954, RMSEA = 0.038, 90% CI [0.033, 0.042]; however, select factor loadings remained weak (see Table 4). When considering content representation of each indicator to the domain of Knowledge and Understanding, and alongside weak factor loadings, we decided to remove the indicator “what it means to be healthy” given that it might be a better indicator of health literacy rather than physical literacy. Two additional indicators – “improve sport skill” and “improve fitness” – also had weak factor loadings. A decision was made to remove one of these indicators and retain the other to enhance construct representation. Although the indicator “improve fitness” had the higher factor loading, we removed it based on its conceptual content that might already be tapped through the cardiorespiratory fitness definition indicator. The indicator “improve sport skill” was retained because, despite its lower factor loading, it showed slightly better discrimination with only 50% getting the answer correct (compared to 80% getting the answer correct for “improve fitness”) and because no other indicators in the Knowledge and Understanding domain tapped into this piece of knowledge.

Lastly, the indicator querying knowledge of the physical activity guidelines had a weak factor loading in Model 2. Interestingly, during the collection of CAPL data, administrators have noted that the response options for this question are too easy for children to guess because the highest response option is the correct response. In fact, 63% of children answered this question correctly, suggesting that the response options should be altered to provide better discrimination. Based on this informal observation, along with considerations of maximizing content representation for the Knowledge and Understanding domain in light of the number of indicators already removed, a decision was made to retain the physical activity guideline indicator given its conceptual relevance. Overall, decisions around retaining weak indicators in the Knowledge and Understanding domain (i.e., “physical activity guidelines” and “improve sport skills”) were made with an eye toward optimizing construct representation while recognizing that these items require refinement if they are to be included in CAPL-2. Model 3 provided a good fit to the data (WLSMVχ²₍₅₎ = 97.39, p < 0.001, CFI = 0.970, RMSEA = 0.043, 90% CI [0.036, 0.051] and was retained as the final model.

Each of the final models described above were placed into one model, allowing for correlations between all four domains. Model 1 was not a good fit (WLSMVχ²₍₇₁₎ = 2887.684, p < 0.001, CFI = 0.871, RMSEA = 0.063, 90% CI [0.061, 0.065]; Table 5). There was a large cross-loading from the domain of Daily Behaviour onto the indicator of physical activity guidelines (Modification Index = 1619.77). Model 2 was estimated including this cross-loading and it provided a good fit (WLSMVχ²₍₇₀₎ = 1221.29, p < 0.001, CFI = 0.947, RMSEA = 0.041, 90% CI [0.039, 0.043]; Table 5). The cross-loading of physical activity guidelines on Daily Behaviour was stronger (λ = 0.42) than on its hypothesized domain of Knowledge and Understanding (λ = 0.28). A decision was made to retain this cross-loading and the original target loading. The question asked children how long children in general should be active and, therefore, conceptually it pertains more to knowledge and understanding than their own actual behaviours. Other problems and possible solutions with this indicator are outlined in the discussion below. Correlations between each domain ranged from 0.08 to 0.76 (p < 0.01; see Table 6). The strongest correlation was between Daily Behaviour, and Motivation and Confidence (r = 0.76). The weakest correlation was between Knowledge and Understanding, and Motivation and Confidence (r = 0.08); however, this correlation was likely only significant because of the very large sample size. Therefore, caution is warranted when interpreting this correlation, as it is very weak and may not be practically meaningful.

In the final model tested, we examined whether an overall physical literacy latent variable accounted for the correlations between the four domains. Using the final model from the four correlated domains model above, we found that the higher-order model had a good fit to the data (WLSMVχ²₍₇₂₎ = 1827.18, p < 0.001, CFI = 0.919, RMSEA = 0.049, 90% CI [0.047, 0.051]; see Fig. 2). Daily Behaviour, Motivation and Confidence, and Physical Competence had the strongest factor loadings from physical literacy. Knowledge and Understanding had a significant, albeit weak, factor loading (see Fig. 2). Therefore, for the next version of CAPL, it is recommended that the domains be re-weighted such that Physical Competence, Daily Behaviour, and Motivation and Confidence have stronger weight (30 points each) than Knowledge and Understanding (10 points). The revised model suggested for CAPL-2, based on the good factor structure suggesting four intercorrelated domains of physical literacy, is shown in Fig. 1d.

Four out of the seven original indicators comprising the domain of Physical Competence were removed because they did not load as strongly as the other indicators. In the first iterations of CAPL, BMI z scores and waist circumference were proposed to serve as indicators of “body composition”, subsumed under a domain labelled “physical fitness” [15]. As a result of the Delphi process, the CAPL model was revised and the domain of “Physical Fitness” was replaced with “Physical Competence”; yet, the indicators of BMI z score and waist circumference were retained under the Physical Competence domain, with a call for future research to assess their contribution. Our finding that the BMI z score was a weak indicator of Physical Competence corroborates previous findings [10], whereas the finding that waist circumference was a weak indicator was a novel finding. Conceptually, because BMI z score and waist circumference were originally proposed as indicators of “body composition” subsumed under “physical fitness”, we are not surprised that they represent weak indicators of the broader domain of Physical Competence. As such, we deemed the removal of these two measures appropriate and suggest that they may be better used as a separate outcome. Two additional indicators (i.e., sit and reach and grip strength) were removed given their weak factor loadings on Physical Competence. It could be argued that the CAMSA, PACER, and plank are better indicators of physical competence and, in turn, of physical literacy, because all tests have been shown to mimic realistic movements and durations of children’s experiences during physical activity. For example, the PACER mirrors the sporadic patterns of starting and stopping by children [23]; the CAMSA enables children to perform simple movements and complex skills in reaction to a changing environment [14]; and the plank assesses torso endurance, which is vital for effective and efficient use of the upper and lower extremities and spinal stability [13]. In contrast, the static flexibility and muscular strength assessments used herein may be less relevant to the physical competence of children of this age, who perform primarily dynamic rather than static movements. It is nonetheless possible that if a few alternative indicators of flexibility and muscle strength were also measured, there could have been additional latent variables that would better characterize these aspects of physical competence. Overall, our results confirm emerging evidence that the domain of Physical Competence pertaining to physical literacy is best characterized by indicators that capture whole body movement, including both skill and fitness components.

For conceptual reasons discussed in the Daily Behaviour domain’s results around the distinctiveness between physical activity and sedentary behaviours (including screen time), a decision was made to remove screen time as an indicator of physical literacy. Additionally, the Daily Behaviour domain is comprised of one subjective (i.e., self-report physical activity) and one direct (i.e., pedometer step counts) indicator. The subjective indicator had a stronger factor loading than that of the pedometer step counts. It is unlikely that this finding stems from the large amount of missing data since the results were similar when the model was rerun with only complete data; nevertheless, there were gender differences between boys and girls who provided valid pedometer data that could have impacted the results. Alternatively, the discrepant factor loadings could be related to what each indicator is actually measuring. In the past, researchers have found that self-reported physical activity and scores from more direct measures may be weakly correlated [36] and tap into somewhat different components of physical activity [35]. For example, children can self-report physical activities that direct measures of physical activity do not capture (such as swimming and cycling, which children report frequently) [35], complex upper body movements, and non-load-bearing activities [37]. Additionally, within the CAPL, the number of days in a typical week engaged in physical activity was assessed via self-report, whereas the number of steps taken over a specific week was assessed via pedometer. These discrepancies between measurements could be causing the differential magnitudes of the factor loadings. Nonetheless, both indicators served as moderately robust indicators of Daily Behaviour and were retained.

An unexpected finding from the Daily Behaviour domain was the large cross-loading from knowledge of physical activity guidelines onto daily behaviour in comparison to its hypothesized domain of Knowledge and Understanding. There are a few possible explanations for this finding. First, it is possible that the instructional stem for the self-report physical activity indicator (which asks the children to report how many days they have engaged in moderate and vigorous physical activity for at least 60 min) actually provided the answer to the subsequent question about how long children should engage in moderate to vigorous physical activity each day (i.e., the correct answer is 60 min). Second, as identified by CAPL administrators through informal feedback, the response option for the physical activity guideline question may have been too easy given that the correct response was the highest value listed. This response option is problematic because it is easy to guess and might have been cued by the self-report physical activity indicator, which includes “60 min” in the instructional stem. In other words, it is possible that the two questions are sharing variance given that children can link the responses of one to the stem of the other. Future research is needed to determine if altering the response options for the knowledge of physical activity guidelines can reduce this problematic cross-loading. Finally, it is worth noting that we did not remove knowledge of physical activity guidelines as an indicator of the Knowledge and Understanding domain and place it as an indicator of Daily Behaviour. Our decision to retain it as an indicator of knowledge and understanding and allow for the cross-loading was based on the conceptual content of the indicator. Because the indicator asks children to report how long kids should be active, we did not feel it was a good indicator of how long the children themselves were actually active.

Five out of 10 indicators from the Knowledge and Understanding domain were removed. Conceptually, many original indicators that have not undergone previous validation do not align with current physical literacy research. For example, knowledge of what it means to be healthy could be an indicator of health literacy rather than physical literacy. Removing self-report screen time from the Daily Behaviour domain and knowledge of screen time guidelines from the Knowledge and Understanding domain can be justified for similar reasons; they may not be conceptually linked to physical literacy given that sedentary behaviours are distinct from physical activity behaviours [33, 34]. Additionally, as noted in the “CAPL Measures” section, we identified an error in the CAPL manual such that an originally conceived indicator for motivation and confidence was erroneously placed within the Knowledge and Understanding domain (i.e., activity preferences). Not surprisingly, asking children their activity preferences was a weak indicator of their knowledge and understanding, and so was removed from that domain. Supplemental analyses indicated that the indicator did indeed load within the Motivation and Confidence domain, although it resulted in a slight deterioration in model fit. Given that the indicator is dichotomous and that the other indicators of Motivation and Confidence provided a superior model fit, combined with an eye toward reducing the participant burden of CAPL, we decided to retain the Motivation and Confidence domain without this indicator.

Knowledge about the safety gear required for physical activity may be a weak indicator of knowledge and understanding, given that the response format did not provide standardized response options. Indeed, this question had an unusual format where children first selected activities they personally engaged in and then, of those, they circled activities requiring safety gear. Consequently, the answers could vary widely.

The question around how to “improve fitness” was removed because a similar question (i.e., “improve sport skills”) was deemed to add more unique content representation whereas “improve fitness” may already be captured by the definition of cardiorespiratory fitness. We retained some indicators of Knowledge and Understanding that had weak factor loadings so as to retain content representation in light of the number of indicators removed. We believe that despite their weaker factor loadings, “improve sport skills” and “knowledge of physical activity guidelines” were querying unique information and that with further redevelopment of the remaining Knowledge and Understanding indicators, they may have stronger factor loadings. The results herein were used to inform the redevelopment of the knowledge and understanding indicators. Notably, the response options for the knowledge of physical activity guidelines indicator were changed, and another fill-in-the-blank question was added to the physical activity comprehension indicator (see Longmuir et al. [38] for further details).

Lastly, one indicator was removed from the Motivation and Confidence domain. “Activity compared to others” was removed to assist in reducing participant burden and reduce construct irrelevant variance attributable to similarity with another indicator. Otherwise, the Motivation and Confidence domain had strong indicators.

The results of this study will inform the creation of CAPL-2 [38]. It is important to note that although we have eliminated indicators that had weak factor loadings on targeted CAPL domains (e.g., waist circumference, grip strength), these indicators in and of themselves may not be bad indicators of what they are purported to asses (e.g., grip strength and sit-and-reach scores can be valid indicators of muscle strength and flexibility, respectively [39, 40]). Therefore, researchers may wish to include these assessments depending on their research question. Additionally, when collecting longitudinal data with the CAPL, it would be prudent to use the same version of CAPL as previously administered such that change over time can be examined unambiguously. Lastly, as noted in the “Background” section, researchers around the world have debated whether or not physical activity should be included within the definition of physical literacy or whether it would be better conceptualized as an outcome of physical literacy. Within the CAPL, we believe that valuing and taking responsibility for engaging in physical activity situates physical activity as an inherent component of physical literacy. Nonetheless, a strength of the CAPL is that researchers are free to use (or not use) domains that are of interest to them, given their theoretical perspective. As such, it is possible to use the CAPL and omit the Daily Behaviour domain to assess physical literacy, as described in the CAPL Manual (available at www.​capl-ecsfp.​ca).

A key finding of this study, when taken in the context of past versions, conceptualizations, and operationalizations of the CAPL, is that physical literacy cannot be reduced only to fitness or motor skill assessments. Indeed, original conceptualizations placed strong emphasis on indicators of physical fitness; yet over the past decade, with emerging validation and physical literacy research, it has become apparent that physical competence encompasses more than fitness and body composition and that motivation and confidence are equally important for physical literacy.

A major strength of this study was the large sample size, which included children from 11 regions across Canada, enabling the assessment of all CAPL indicators. Notwithstanding these strengths, limitations of this study should be acknowledged. There was a large amount of missing data on the pedometer daily step count scores, and there were gender differences in the amount of missing data for pedometers. Although modern procedures were used to handle missing data, it is not known to what extent the missing data influenced the final conclusions. Nonetheless, sensitivity analysis of the final higher-order model without missing data (i.e., using listwise deletion n = 5073) revealed a similar pattern and magnitude of results to those we report herein. Second, although our decisions to remove or retain indicators were made based on statistical criteria, theory, and considerations of content representation, many of these decisions were ultimately subjective in nature. It is possible that alternative models would have fit the data well. Moreover, although we used confirmatory factor analyses, the post-hoc modifications were more exploratory in that they were based on discussion, consideration of theory, and statistical criteria. It is paramount that researchers continue to examine the factor structure of the CAPL found herein using new samples in different contexts. Researchers may wish to test alternative models that align with other definitions of physical literacy theory [4]. Additionally, researchers may wish to extend validity evidence based on internal structure by examining grade and gender invariance to determine if CAPL scores are being measured in the same way across genders and grade levels. Finally, the measures in the Knowledge and Understanding and the Motivation and Confidence domains are self-reported and therefore susceptible to social desirability or recall bias.