Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Construct Validity Evidence for the Muscle Strengthening Activity Scale (MSAS)

Peter D. Hart

Health Promotion Research, Havre, MT 59501-7751

Abstract: Background: The 2018 (2nd edition) Physical Activity Guidelines for Americans states that adults should participate in muscle strengthening activity (MSA) of at least moderate intensity using all major muscle groups on two or more days a week. However, these guidelines do not promote specific types of MSA such as muscular strength training or muscular endurance training. This ambiguity, in part, is due to the lack of evidence linking specific types of MSA to health outcomes. And this lack of evidence, in part, is due to the inability to measure varying MSA behavior. This study reports the construct validity evidence for the MSA Scale (MSAS). Methods: The following research consists of a second development stage presenting validity evidence for the MSAS. Previous research indicates that seven items can measure three MSA dimensions: a three-item muscular strength dimension, a three-item muscular endurance dimension, and a single-item body weight exercise dimension. The current research used both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to examine the MSAS construct validity. Results: EFA indicated a two-factor structure explained 100% of the common variance among the 6 strength and endurance items (3 items per factor with all loadings > .52). The first factor was defined as strength and the second endurance. CFA indicated the two-factor MSAS measurement model had adequate fit (χ²/df = 4.24, GFI = 0.97, CFI = 0.92, and RMSEA = 0.09) with strength and endurance significantly (p < .001) predicting all observed variables. Factor strength scores were strongly correlated with strength sum scale scores and weakly correlated with endurance and body sum scale scores. Similarly, factor endurance scores were strongly correlated with endurance sum scale scores and weakly correlated with strength and body sum scale scores. Conclusion: The seven-item MSAS is a simple and valid tool for measuring MSA behavior in adults. Two additional items are included in the MSAS to quantify MSA participation.

Keywords: Muscle strengthening activity (MSA), Construct validity, Exploratory factor analysis (EFA), Confirmatory factor analysis (CFA)

Received September 18, 2019; Revised November 02, 2019; Accepted November 10, 2019

Citation: Peter D. Hart, Construct Validity Evidence for the Muscle Strengthening Activity Scale (MSAS). American Journal of Public Health Research, 7 (5). 189-193. January 2019. doi:10.12691/ajphr-7-5-4

1. Introduction

The 2018 Physical Activity Guidelines for Americans (2nd edition) reinforces its previous recommendation for the promotion of muscle strengthening activity (MSA) of at least moderate intensity using all major muscle groups on two or more days a week by all adults [1]. For many Americans, however, this recommendation remains vague. For example, guidelines specifically state that no specific amount of muscle strengthening time is recommended [2]. Additionally, no clear program design variables are given in terms of MSA benefits (i.e., muscular strength or muscular endurance). The likely reason for this ambiguity is the lack of evidence linking the various types of MSA to health outcomes. Furthermore, it is also likely that evidence associating specific types of MSA with health outcomes is sparse because there is no valid assessment tool available to measure the different types of MSA behavior. The purpose of this study was to fill this aforementioned gap in the MSA literature. Specifically, this research examined the construct validity of a newly developed assessment tool to measure MSA behavior, the Muscle Strengthening Activity Scale (MSAS).

2. Materials & Methods

2.1. Study and Scale Development Procedures

The development procedures related to the MSAS have been explained in detail elsewhere [3]. Briefly, A total of N=1,240 adults agreed to take the MSAS using an electronic survey tool. Of which, N=400 adults indicated participating in regular MSA. After a first-stage reliability and item analysis, the pilot version of the MSAS resulted in a seven-item scale measuring three distinct MSA dimensions: a three-item muscular strength dimension, a three-item muscular endurance dimension, and a single-item body weight exercise dimension. The final format of the MSAS resulted as follows. A screener question is included at the top of the instrument to ask individuals if they regularly participate in MSA. Those responding “No” are instructed not to continue to the rest of the instrument. Part I of the MSAS contains the seven scale items, each with the same five-category rating rule ranging from “Never true” to “Always true”. Item stems consist of personalized statements regarding muscular strength training behavior, muscular endurance training behavior, and body weight exercise training behavior. For example, “I often exercise my muscles with heavy weight that I can lift 1 to 8 times”. Part II of the MSAS contains two items asking participants about their frequency and duration of MSA participation. These part II items are included to quantify amounts of MSA performed, however, these items are not evaluated in this study. At the bottom of the MSAS, directions are given to obtain strength, endurance, and body attribute scores as well as an MSA participation score.

2.2. Statistical Analyses

The statistical analysis plan for the current study followed five steps [4]. First, inter-item correlation coefficients were computed. Inter-item correlation coefficients are bivariate correlation coefficients between each pair of MSAS items with a suggested strength cutoff of r > .30. This step also included testing the adequacy of the correlation matrix for factor analysis. Second, exploratory factor analysis (EFA) was performed as a subjective means of construct validity evidence. EFA uses common variance among the items to extract a parsimonious set of factors. Initially, all seven items were included in the EFA, however, since the body item showed a weak loading to the endurance factor, it was removed from the analysis. EFA extraction method was by maximum likelihood (ML) and factors were rotated using an orthogonal varimax technique. Third, convergent and divergent validity evidence was obtained by examining correlations between EFA scores and MSAS dimension sum scores. Fourth, confirmatory factor analysis (CFA) was performed as an objective test of the hypothesized six-item two-factor MSAS measurement model. Model fit was assessed using the following statistics and criteria: comparative fit index (CFI > 0.90), root mean square error of approximation (RMSEA < 0.10), goodness of fit index (GFI > 0.90), adjusted goodness of fit index (AGFI > 0.90), Tucker-Lewis index (TLI > 0.90), normed fit index (NFI > 0.90), and Akaike’s information criterion (AIC, relatively lower values indicate better fit). As well, the chi-square statistic (χ²) to degrees of freedom (df) ratio (i.e., normed chi-square) was used with acceptable criteria of less than 5.0 [5,6,7]. Fifth and lastly, the analysis of convergent and divergent validity was again obtained by examining correlations between CFA scores and MSAS dimension sum scores. All correlations were Pearson coefficients from the SAS CORR procedure and Python Numpy package. EFA was conducted using the SAS FACTOR procedure and Python factor_analyzer package. Finally, CFA was conducted using the SAS CALIS procedure and Python factor_analyzer package. SAS version 9.4 and Python version 3.7 were used to analyze all data in duplicate, to ensure consistency [8,9,10,11].

3. Results

Table 1 contains bivariate Pearson correlation coefficients for the six items associated with the muscular endurance and muscular strength MSAS subscales. As expected, the largest correlations are between items within each theorized MSAS subscale. The only correlation coefficient not meeting the recommended convergent validity criteria (r > .30) is the correlation between items 2 and 3 of the strength subscale (r = .268), albeit very close and significant (p < .05). Additionally, Bartlett’s test of sphericity, testing the null hypothesis that the observed correlation matrix is equal to the identity matrix, was large and significant, χ² = 343.2, p < .001. As well, Kaiser's measure of sampling adequacy (MSA), assessing the factorability of the observed variables, was acceptable, MSA = .664.

Table 2 contains results from the EFA with varimax rotated factor pattern for the MSAS. Results indicated a two-factor structure explained 100% of the common variance among the 6 strength and endurance items. The factor structure was simple with three items strongly loading (loadings > .52) to each factor and weak cross-loadings (loadings < .31). The first factor was defined as strength and the second as endurance, as expected, and hereafter referred to as such. Table 3 contains bivariate correlation coefficients between the three dimension MSAS sum scores and the two EFA factor scores. EFA strength scores (EFA2) were strongly correlated with strength (r = .983, p < .05) sum scale scores and weakly correlated with endurance (r = .098, p = .051) and body (r = -.187, p < .05) sum scale scores. Similarly, EFA endurance scores (EFA1) were strongly correlated with endurance (r = .985, p < .05) sum scale scores and weakly correlated with strength (r = .164, p < .05) and body (r = .293, p < .05) sum scale scores.

Table 1. Correlation matrix of the two MSAS subscale items (N=400)

Table 2. Exploratory factor analysis (EFA) rotated factor pattern for the two MSAS subscales (N=400)

Table 3. Correlations between the two EFA factor scores and the three MSAS sum scores (N=400)

Figure 1. Confirmatory factor analysis (CFA) of the two-factor MSAS measurement model

Figure 1 displays the MSAS measurement model, depicting two latent factors (three items each), as indicated by the EFA. A two-factor measurement model showed to be needed over a single-factor model (χ² for difference = 104.9, df=1, p < .001). Additionally, all items were significantly (p < .001) predicted by the strength and endurance factors with positive loadings indicating their direct relationship. Table 4 contains fit statistics for the two-factor MSAS measurement model and indicates adequate fit (χ²/df = 4.24, GFI = 0.97, CFI = 0.92, and RMSEA = 0.09). Table 5 contains correlations between the three dimension MSAS sum scores and the two CFA factor scores. CFA strength scores (CFA2) were strongly correlated with strength (r = .985, p < .05) sum scale scores and weakly correlated with endurance (r = .278, p < .05) and body (r = -.114, p < .05) sum scale scores. Similarly, CFA endurance scores (CFA1) were strongly correlated with endurance (r = .972, p < .05) sum scale scores and weakly correlated with strength (r = .274, p < .05) and body (r = .236, p < .05) sum scale scores.

Table 4. CFA results for the MSAS measurement model

Table 5. Correlations between the two CFA factor scores and the three MSAS scores (N=400)

4. Discussion

The ultimate question under study here is, does the MSAS have acceptable measurement properties? Results from this study suggest the answer is ‘yes’. Using both subjective and objective statistical approaches, this study has shown that the MSAS measures three distinct traits. Furthermore, this study has shown that the three simple sum scores from the MSAS provide the same measurement properties. Given these statistical results, it can be determined that the three distinct latent traits found in this study are indeed the same traits they were developed to measure. That is, a three-item muscular strength trait, a three-item muscular endurance trait, and a single-item body weight exercise trait.

Previous research on the MSAS has also provided positive psychometric evidence [3]. That is, the MSAS was developed using a multi-stage content validity procedure where a large pool of items were built using content experts and a focus group and subsequently reduced and modified using a series of pilot tests and item analyses. Furthermore, this previous research showed that MSAS subscale (strength and endurance) items were reliable (internally consistent) with the body weight item resolved to measure its own single-item trait. Item category scales (five-category rating rule ranging from “Never true” to “Always true”) were also modified and showed to finally function well in this previous research.

Therefore, the present totality of evidence concerning the MSAS clearly supports its validity and reliability in measuring three MSA behaviors: muscular strength, muscular endurance, and body weight exercise. Future studies are still warranted, however. One recommendation is to study the extent to which the MSAS holds up to the critical power of modern psychometric examination (i.e., item-response theory (IRT)). A second recommendation is to further evaluate MSAS construct validity by examining its ability to separate MSA participants with known differences in MSA behavior (e.g., power lifters vs. circuit trainers vs. yogis). A third recommendation is to study the extent to which the MSAS can detect changes in MSA behavior (i.e., specificity), be it via intervention or personal willpower. A final recommendation is to evaluate part II of the MSAS for its ability to validly quantify participation in MSA.

5. Conclusions

The seven-item MSAS is a simple and valid tool for measuring specific MSA behavior in adults. Two additional items are included in the MSAS to quantify MSA participation, but to date, have not been validated. Caution should be taken for item missing values, since this research used item summation for trait scores. The MSAS is free to use without restrictions, providing proper citation.

References

Appendix: (download from: http://www.fitmetrics.org/MSAS.pdf)

Figure

[1]   | Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The physical activity guidelines for Americans. JAMA. 2018 Nov 20; 320(19): 2020-8.
[2]   | U.S. Department of Health and Human Services. Physical Activity Guidelines for Americans, 2nd edition. Washington, DC: U.S. Department of Health and Human Services; 2018.
[3]   | Hart PD. Development and item analysis of a multidimensional scale to measure muscle strengthening behavior: The Muscle Strengthening Activity Scale (MSAS). EAS Journal of Psychology and Behavioural Sciences. 2019. 1(2): 29-35.
[4]   | Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: Pearson College Division.
[5]   | Schumacker RE, Lomax RG. A beginner’s guide to structural equation modeling. Psychology Press. 2016, p 73.
[6]   | Hooper D, Coughlan J, Mullen M. Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods. 2008; 6(1):53-60.
[7]   | MacCallum, R.C., Browne, M.W. and Sugawara, H.M., 1996. Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), p.130.
[8]   | Cody, R. P., & Smith, J. K. (2006). Applied Statistics & SAS Programming. Prentice Hall.
[9]   | Hatcher, L. and O'Rourke, N., 2013. A step-by-step approach to using SAS for factor analysis and structural equation modeling. SAS Institute.
[10]   | Tan, T.K., SUGAS Paper 2010 Building Latent Growth Models Using PROC CALIS: A Structural Equation Modeling Approach Teck Kiang Tan, Trivina Kang, David Hogan Centre for Research in Pedagogy and Practice, Nanyang Technological University, Singapore.
[11]   | McKinney, W., 2012. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media, Inc.
[12]   | Biggs, J., 2019. Factor_analyzer documentation. Release 0.3.1.