Journal articles: 'Repeated-measures ANOVA'

1

Ziegel, Eric R., and E. Girden. "ANOVA: Repeated Measures." Technometrics 35, no. 4 (November 1993): 464. http://dx.doi.org/10.2307/1270301.

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Johnson, Lester W., and Ellen R. Girden. "ANOVA: Repeated Measures." Journal of Marketing Research 32, no. 2 (May 1995): 243. http://dx.doi.org/10.2307/3152054.

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McCulloch, Charles E. "Repeated Measures ANOVA, R.I.P.?" CHANCE 18, no. 3 (June 2005): 29–33. http://dx.doi.org/10.1080/09332480.2005.10722732.

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Johnson, Lester W. "Book Review: Anova: Repeated Measures." Journal of Marketing Research 32, no. 2 (May 1995): 243. http://dx.doi.org/10.1177/002224379503200213.

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Muller, Keith E., and Curtis N. Barton. "Approximate Power for Repeated-Measures ANOVA Lacking Sphericity." Journal of the American Statistical Association 84, no. 406 (June 1989): 549–55. http://dx.doi.org/10.1080/01621459.1989.10478802.

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CHEKALUK, E., T. P. HUTCHINSON, and D. CAIRNS. "Repeated measures ANOVA for responses developing over time." European Journal of Anaesthesiology 15, no. 3 (May 1998): 381–82. http://dx.doi.org/10.1097/00003643-199805000-00029.

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7

Jennings, Earl. "Models for Pretest-Posttest Data: Repeated Measures ANOVA Revisited." Journal of Educational Statistics 13, no. 3 (September 1988): 273–80. http://dx.doi.org/10.3102/10769986013003273.

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Eight models that can be used to analyze data from a pretest-posttest design are discussed. Artificial data are used to demonstrate that comparisons among some of the models generate results identical to those obtained by using repeated measures ANOVA. Such results are shown to be potentially misleading. Repeated measures ANOVA is not recommended with pretest-posttest data.

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Potvin, Patrick J., and Robert W. Schutz. "Statistical power for the two-factor repeated measures ANOVA." Behavior Research Methods, Instruments, & Computers 32, no. 2 (June 2000): 347–56. http://dx.doi.org/10.3758/bf03207805.

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Jennings, Earl. "Models for Pretest-Posttest Data: Repeated Measures ANOVA Revisited." Journal of Educational Statistics 13, no. 3 (1988): 273. http://dx.doi.org/10.2307/1164655.

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Grice, James W., David Philip Arthur Craig, and Charles I. Abramson. "A Simple and Transparent Alternative to Repeated Measures ANOVA." SAGE Open 5, no. 3 (July 10, 2015): 215824401560419. http://dx.doi.org/10.1177/2158244015604192.

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11

Rasmussen, Jeffrey Lee. "ANOVA MultiMedia: A Program for Teaching ANOVA Designs." Teaching of Psychology 23, no. 1 (February 1996): 55–56. http://dx.doi.org/10.1207/s15328023top2301_15.

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A multimedia program for teaching analysis of variance (ANOVA) designs is described. The program tests students' understanding of independent groups and repeated measures variables, as well as the source and degrees of freedom columns of the ANOVA source table. The program uses colorful images, brief animations, interactive tasks, and immediate feedback. Information about availability is provided.

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Marini, Richard P. "Repeated Measures Analyses for Tree Fruit Experiments." HortScience 30, no. 4 (July 1995): 845C—845. http://dx.doi.org/10.21273/hortsci.30.4.845c.

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Experiments with perennial crops often span several years, and a response variable may be measured on the same plant at several points in time. Such data are often analyzed as a split-plot design, taking time as the split-plot factor. In other cases, separate analyses are performed for each time. The mathematical conditions required for validity of these types of analyses might not hold because measurements repeated on the same plant are not independent. Annual trunk cross-sectional-area (TCSA) measurements from a peach tree training experiment will be used to compare two methods of analyses. The 6-year experiment was a factorial of two heading heights at planting (low vs. high) and two tree forms (central leader vs. open vase). Univariate analysis of variance (ANOVA) and a multivariate repeated measures analysis (MANOVA) was performed. Main effects and interactions were more often significant with ANOVA than with MANOVA. ANOVA performed each year inflated the probability of falsely rejecting a true null hypothesis (Type I error), and was not appropriate for this data set.

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Park, Eunjeong. "The comparison between repeated measures ANOVA and multilevel modeling to investigate the effectiveness of language instruction." Advances in Social Sciences Research Journal 7, no. 6 (June 27, 2020): 426–37. http://dx.doi.org/10.14738/assrj.76.8438.

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The purpose of the current study is to compare two different statistical analyses—repeated measures analysis of variance (ANOVA) and multilevel modeling (MLM)—with regard to international second language (L2) students’ lexico-grammatical writing gains in the interventions. Fifty college students’ lexico-grammatical writing gains in the pre-test, immediate and delayed post-tests were collected from three groups—one control and two intervention groups. Research questions include: (1) Is there a change in the dependent variable over time through repeated measures ANOVA?; (2) How much on average do the individuals change over time through MLM?; and (3) What are the differences of the results between repeated measures ANOVA and MLM? Repeated measures ANOVA and MLM were conducted separately to answer the research questions. Both analyses revealed that there was no difference of the interventions over time. In terms of methodological considerations, however, the two analyses exhibited the results in a different manner. Repeated measures ANOVA requires several assumptions (e.g., homoschedasticity and sphericity) while MLM does not need the requirement of the data as long as it contains different periodic datasets. Furthermore, MLM enables researchers to investigate the rate of individual growth. This paper offers methodological insight into adequate quantitative analyses to investigate the effectiveness of language acquisition and learning.

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Kim, Hae-Young. "Statistical notes for clinical researchers: A one-way repeated measures ANOVA for data with repeated observations." Restorative Dentistry & Endodontics 40, no. 1 (2015): 91. http://dx.doi.org/10.5395/rde.2015.40.1.91.

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Liu, Y., and R. W. Schutz. "TYPE I ERROR RATES IN REPEATED MEASURES ANOVA WHEN USING RATIO VARIABLES." Medicine & Science in Sports & Exercise 33, no. 5 (May 2001): S170. http://dx.doi.org/10.1097/00005768-200105001-00965.

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Park, Ilhyeok, and Robert W. Schutz. ""Quick and Easy" Formulae for Approximating Statistical Power in Repeated Measures ANOVA." Measurement in Physical Education and Exercise Science 3, no. 4 (October 1999): 249–70. http://dx.doi.org/10.1207/s15327841mpee0304_5.

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Wilcox, Rand R., H. J. Keselman, Jan Muska, and Robert Cribbie. "Repeated measures ANOVA: Some new results on comparing trimmed means and means." British Journal of Mathematical and Statistical Psychology 53, no. 1 (May 2000): 69–82. http://dx.doi.org/10.1348/000711000159187.

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Gibbons, James A., and Robert D. Sherwood. "Repeated Measures/Randomized Blocks Anova through the Use of Criterion-Scaled Regression Analysis." Educational and Psychological Measurement 45, no. 4 (December 1985): 711–24. http://dx.doi.org/10.1177/0013164485454001.

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Thomas, D. Roland, and Bruno D. Zumbo. "Difference Scores From the Point of View of Reliability and Repeated-Measures ANOVA." Educational and Psychological Measurement 72, no. 1 (June 2, 2011): 37–43. http://dx.doi.org/10.1177/0013164411409929.

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Wilcox, Rand R., and H. J. Keselman. "Repeated measures one-way ANOVA based on a modified one-step M-estimator." British Journal of Mathematical and Statistical Psychology 56, no. 1 (May 2003): 15–25. http://dx.doi.org/10.1348/000711003321645313.

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Kherad-Pajouh, Sara, and Olivier Renaud. "A general permutation approach for analyzing repeated measures ANOVA and mixed-model designs." Statistical Papers 56, no. 4 (August 20, 2014): 947–67. http://dx.doi.org/10.1007/s00362-014-0617-3.

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22

Raadt, Jay Schyler. "The Pressing Need to Test for Autocorrelation: Comparison of Repeated Measures ANOVA and Interrupted Time Series Autoregressive Models." Journal of Methods and Measurement in the Social Sciences 9, no. 2 (June 12, 2019): 22–31. http://dx.doi.org/10.2458/v9i2.23487.

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Neglecting to measure autocorrelation in longitudinal research methods such as Repeated Measures (RM) ANOVA produces invalid results. Using simulated time series data varying on autocorrelation, this paper compares the performance of repeated measures analysis of variance (RM ANOVA) to interrupted time series autoregressive integrated moving average (ITS ARIMA) models, which explicitly model autocorrelation. Results show that the number of RM ANOVA signaling an intervention effect increase as autocorrelation increases whereas this relationship is opposite using ITS ARIMA. This calls the use of RM ANOVA for longitudinal educational research into question as well as past scientific results that used this method, exhorting educational researchers to investigate the use of ITS ARIMA.

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Kristensen, Michael, and Thomas Hansen. "Statistical analyses of repeated measures in physiological research: a tutorial." Advances in Physiology Education 28, no. 1 (March 2004): 2–14. http://dx.doi.org/10.1152/advan.00042.2003.

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Experimental designs involving repeated measurements on experimental units are widely used in physiological research. Often, relatively many consecutive observations on each experimental unit are involved and the data may be quite nonlinear. Yet evidently, one of the most commonly used statistical methods for dealing with such data sets in physiological research is the repeated-measurements ANOVA model. The problem herewith is that it is not well suited for data sets with many consecutive measurements; it does not deal with nonlinear features of the data, and the interpretability of the model may be low. The use of inappropriate statistical models increases the likelihood of drawing wrong conclusions. The aim of this article is to illustrate, for a reasonably typical repeated-measurements data set, how fundamental assumptions of the repeated-measurements ANOVA model are inappropriate and how researchers may benefit from adopting different modeling approaches using a variety of different kinds of models. We emphasize intuitive ideas rather than mathematical rigor. We illustrate how such models represent alternatives that 1) can have much higher interpretability, 2) are more likely to meet underlying assumptions, 3) provide better fitted models, and 4) are readily implemented in widely distributed software products.

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Lee, Hwa-Jung, and Suk-Bok Kang. "Analysis of latent growth model using repeated measures ANOVA in the data from KYPS." Journal of the Korean Data and Information Science Society 24, no. 6 (November 30, 2013): 1409–19. http://dx.doi.org/10.7465/jkdi.2013.24.6.1409.

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Macey, Paul M., Philip J. Schluter, Katherine E. Macey, and Ronald M. Harper. "Detecting variable responses in time-series using repeated measures ANOVA: Application to physiologic challenges." F1000Research 5 (July 8, 2016): 563. http://dx.doi.org/10.12688/f1000research.8252.2.

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We present an approach to analyzing physiologic timetrends recorded during a stimulus by comparing means at each time point using repeated measures analysis of variance (RMANOVA). The approach allows temporal patterns to be examined without an a priori model of expected timing or pattern of response. The approach was originally applied to signals recorded from functional magnetic resonance imaging (fMRI) volumes-of-interest (VOI) during a physiologic challenge, but we have used the same technique to analyze continuous recordings of other physiological signals such as heart rate, breathing rate, and pulse oximetry. For fMRI, the method serves as a complement to whole-brain voxel-based analyses, and is useful for detecting complex responses within pre-determined brain regions, or as a post-hoc analysis of regions of interest identified by whole-brain assessments. We illustrate an implementation of the technique in the statistical software packages R and SAS. VOI timetrends are extracted from conventionally preprocessed fMRI images. A timetrend of average signal intensity across the VOI during the scanning period is calculated for each subject. The values are scaled relative to baseline periods, and time points are binned. In SAS, the procedure PROC MIXED implements the RMANOVA in a single step. In R, we present one option for implementing RMANOVA with the mixed model function “lme”. Model diagnostics, and predicted means and differences are best performed with additional libraries and commands in R; we present one example. The ensuing results allow determination of significant overall effects, and time-point specific within- and between-group responses relative to baseline. We illustrate the technique using fMRI data from two groups of subjects who underwent a respiratory challenge. RMANOVA allows insight into the timing of responses and response differences between groups, and so is suited to physiologic testing paradigms eliciting complex response patterns.

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Rovine, Michael J., and Paul A. McDermott. "Latent Growth Curve and Repeated Measures ANOVA Contrasts: What the Models are Telling You." Multivariate Behavioral Research 53, no. 1 (December 8, 2017): 90–101. http://dx.doi.org/10.1080/00273171.2017.1387511.

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27

Vasey, Michael W., and Julian F. Thayer. "The Continuing Problem of False Positives in Repeated Measures ANOVA in Psychophysiology: A Multivariate Solution." Psychophysiology 24, no. 4 (July 1987): 479–86. http://dx.doi.org/10.1111/j.1469-8986.1987.tb00324.x.

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Macey, Paul M., Philip J. Schluter, Katherine E. Macey, and Ronald M. Harper. "Detecting variable responses within fMRI time-series of volumes-of-interest using repeated measures ANOVA." F1000Research 5 (April 5, 2016): 563. http://dx.doi.org/10.12688/f1000research.8252.1.

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We present an approach to analyzing fMRI timetrends from volumes-of-interest (VOI) within and between subject groups using repeated measures analysis of variance (RMANOVA), which allows temporal patterns to be examined without an a priori model of expected timing or pattern of response. The method serves as a complement to whole-brain voxel-based analyses, and is useful for detecting complex responses within pre-determined brain regions, or as a post-hoc analysis of regions of interest identified by whole-brain assessments. We illustrate an implementation of the technique in the statistical software package SAS. VOI timetrends are extracted from conventionally preprocessed fMRI images. A timetrend of average signal intensity across the VOI during the scanning period is calculated for each subject. The values are scaled relative to baseline periods, imported into SAS, and the procedure PROC MIXED implements the RMANOVA. The ensuing results allow determination of significant overall effects, and time-point specific within- and between-group responses relative to baseline. We illustrate the technique using fMRI data from two groups of subjects who underwent a respiratory challenge. RMANOVA allows insight into the timing of responses and response differences between groups, and so is suited to fMRI paradigms eliciting complex response patterns.

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29

Silverberg, Arthur R. "A simulation study comparing two approximations for a quasit-quantile, used in repeated measures anova." Statistics in Medicine 13, no. 16 (August 30, 1994): 1657–63. http://dx.doi.org/10.1002/sim.4780131606.

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Zimmerman, Donald W., and Bruno D. Zumbo. "Relative Power of the Wilcoxon Test, the Friedman Test, and Repeated-Measures ANOVA on Ranks." Journal of Experimental Education 62, no. 1 (July 1993): 75–86. http://dx.doi.org/10.1080/00220973.1993.9943832.

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Lohse, Keith R., Jincheng Shen, and Allan J. Kozlowski. "Modeling Longitudinal Outcomes: A Contrast of Two Methods." Journal of Motor Learning and Development 8, no. 1 (April 1, 2020): 145–65. http://dx.doi.org/10.1123/jmld.2019-0007.

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Background: Repeated measures analysis of variance (ANOVA) is frequently used to model longitudinal data but does not appropriately account for within-person correlations over time, does not explicitly model time, and cannot flexibly handle missing data. In contrast, mixed-effects regression addresses these limitations. In this commentary, we compare these two methods using openly available tools. Methods: We emulated a real developmental study of elite skiers, tracking national rankings from 2011 to 2018. We constructed unconditional models of time (establishing the “pattern” of change) and conditional models of time (identifying factors that affect change over time), and contrasted these models against comparable repeated measures ANOVAs. Results: Mixed-effects regression allowed for linear and non-linear modeling of the skiers’ longitudinal trajectories despite missing data. Missing data is still a concern in mixed-effects regression models, but in the present dataset missingness could be accounted for by skiers’ ages, satisfying the missing at random assumption. Discussion: Although ANOVA and mixed-effects regression are both suitable for time-series data, their applications differ. ANOVA will be most parsimonious when the research question focuses on group-level mean differences at arbitrary time points. However, mixed-effects regression is more suitable where time is inherently important to the outcome, and where individual differences are of interest.

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Lee, Kyung Su, Tae Hyeong Kim, Jae Ou Lee, and Yong Lim Lee. "Repeated Measures ANOVA for Fine Dust of Industrial Complex 3 and 4 in Daejeon, South Korea." Journal of the Korean Society of Hazard Mitigation 19, no. 4 (August 31, 2019): 235–45. http://dx.doi.org/10.9798/kosham.2019.19.4.235.

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Dorn, K. T. "MANOVA AND PROFILE ANALYSIS: ALTERNATIVE TO REPEATED MEASURES ANOVA AND POST-HOC PAIRED T-TESTS 1115." Medicine &amp Science in Sports &amp Exercise 28, Supplement (May 1996): 187. http://dx.doi.org/10.1097/00005768-199605001-01113.

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Rohay, Jeffrey M., Gary Marsh, Susan Sereika, Sati Mazumdar, and Jacqueline Dunbar-Jacob. "37P The effects of aggregation on medication compliance history in repeated measures anova: A simulation study." Controlled Clinical Trials 15, no. 3 (June 1994): 98. http://dx.doi.org/10.1016/0197-2456(94)90165-1.

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Choi, Seung-oh, Harry J. Meeuwsen, Ron French, and Jill Stenwall. "Learning and Control of Simple Aiming Movements by Adults with Profound Mental Retardation." Adapted Physical Activity Quarterly 16, no. 2 (April 1999): 167–77. http://dx.doi.org/10.1123/apaq.16.2.167.

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Behavioral, response outcome, and response kinematic measures were analyzed for 6 adults (5 males and 1 female) with profound mental retardation (PMR). Participants performed 30 error-free simple linear aiming movements on a digitizing tablet during 7 acquisition, 3 retention, and 4 transfer days. A one-way ANOVA on the number of trials to reach 30 error-free responses revealed that adults with PMR improved, learned the skill, and transferred it to a new situation. The 2 × 3 × 3 (Phase × Day × Block) repeated measures ANOVAs for response outcome and kinematic measures indicated that participants were able to initiate movement faster with practice. However, practice did not result in changes in kinematic response measures.

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Nunes, João Pedro, and Giovanna F. Frigoli. "THE MISUSE OF SYNTAX ALTERATION FOR GEE AND ANOVA POST-HOC ANALYSES IN SPSS." Científic@ - Multidisciplinary Journal 7, no. 1 (February 3, 2020): 1–6. http://dx.doi.org/10.29247/2358-260x.2020v7i1.4164.

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The online support of IBM SPSS proposes that users alter the syntax when performing post-hoc analyses for interaction effects of ANOVA tests. Other authors also suggest altering the syntax when performing GEE analyses. This being done, the number of possible comparisons (k value) is also altered, therefore influencing the results from statistical tests that k is a component of the formula, such as repeated measures-ANOVA and Bonferroni post-hoc of ANOVA and GEE. This alteration also exacerbates type I error, producing erroneous results and conferring potential misinterpretations of data. Reasoning from this, the purpose of this paper is to report the misuse and improper handling of syntax for ANOVAs and GEE post-hoc analyses in SPSS and to illustrate its consequences on statistical results and data interpretation.

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Inglis, Sue. "Influence in and around Interuniversity Athletics." Journal of Sport Management 5, no. 1 (January 1991): 18–33. http://dx.doi.org/10.1123/jsm.5.1.18.

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This paper addresses the degree of influence exerted on athletic programs from internal and external sources. Using survey data, internal influence was assessed by the athletic administrators indicating their perceptions of their influence in decision-making activities. Factor analysis yielded three factors (administrative, strategic, and marketing decision types) that were used in repeated-measures ANOVA procedures with administrative level as the independent measure and decision types as the dependent measures. Significant results are discussed in relation to the theoretical concepts of decision types, gender, and hierarchical position. External influence was assessed by the athletic administrators and university presidents indicating their perceptions of the degree of influence exerted by external groups on the athletic program. Repeated-measures ANOVA procedures with subsequent Scheffé post hoc analyses where appropriate were used. The results are discussed in relation to the hierarchical position of the respondents and levels of influence exerted by the external groups.

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Gladwin, Thomas Edward. "An implementation of N-way repeated measures ANOVA: Effect coding, automated unpacking of interactions, and randomization testing." MethodsX 7 (2020): 100947. http://dx.doi.org/10.1016/j.mex.2020.100947.

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Krueger, Charlene, and Lili Tian. "A Comparison of the General Linear Mixed Model and Repeated Measures ANOVA Using a Dataset with Multiple Missing Data Points." Biological Research For Nursing 6, no. 2 (October 2004): 151–57. http://dx.doi.org/10.1177/1099800404267682.

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Longitudinal methods are the methods of choice for researchers who view their phenomena of interest as dynamic. Although statistical methods have remained largely fixed in a linear view of biology and behavior, more recent methods, such as the general linear mixed model (mixed model), can be used to analyze dynamic phenomena that are often of interest to nurses. Two strengths of the mixed model are (1) the ability to accommodate missing data points often encountered in longitudinal datasets and (2) the ability to model nonlinear, individual characteristics. The purpose of this article is to demonstrate the advantages of using the mixed model for analyzing nonlinear, longitudinal datasets with multiple missing data points by comparing the mixed model to the widely used repeated measures ANOVA using an experimental set of data. The decision-making steps in analyzing the data using both the mixed model and the repeated measures ANOVA are described.

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Shaulis, Dahn, Lawrence A. Golding, and Richard D. Tandy. "Reliability of the AAHPERD Functional Fitness Assessment across Multiple Practice Sessions in Older Men and Women." Journal of Aging and Physical Activity 2, no. 3 (July 1994): 273–79. http://dx.doi.org/10.1123/japa.2.3.273.

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This study assessed the relative and absolute reliability of the five tests in the AAHPERD functional fitness assessment for men and women over 60 years of age. Twenty-eight apparently healthy subjects, ages 60 to 81, were tested three times during a 2-week period on each item in the test battery: sit and reach flexibility, body agility, coordination, strength/endurance, and half-mile walk. Relative reliability was assessed for both sexes via intraclass correlation coefficient. Absolute reliability was evaluated using repeated measures ANOVA. Intraclass correlations among sessions for men and women, respectively, were 0.97 and 0.98 for flexibility, 0.98 and 0.96 for body agility, 0.89 and 0.71 for coordination, 0.94 and 0.81 for strength/endurance, and 0.99 and 0.96 for the walk. Repeated measures ANOVAs with Tukey’s post hoc tests revealed improved performance from repeated practice sessions in all tests, although the improvement was not consistent between tests. Although the tests have high intraclass correlations, researchers using the test battery should include a random control group to assess the effects of training.

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Amirsalari, Sudabeh, Zahra Behboodi Moghadam, Ziba Taghizadeh, Mina Naghi Jafar Abadi, Parichehr Sabaghzadeh Irani, Saied Goodarzi, and Hadi Ranjbar. "The Effect of Garlic Tablets on the Endometriosis-Related Pains: A Randomized Placebo-Controlled Clinical Trial." Evidence-Based Complementary and Alternative Medicine 2021 (July 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/5547058.

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Endometriosis is a common chronic inflammatory disease. Garlic contains components that have antiproliferative, anti-inflammatory, and antioxidative effects. The current study aimed to evaluate the effectiveness of garlic on endometriosis symptoms. This was a randomized placebo-controlled triple-blind clinical trial. A convenience sample of 60 women was randomly allocated into two groups. The intervention group received usual care supplemented with 400 mg garlic tablets, and the placebo group received identical placebo tablets. A four-part Visual Analogue Scale (VAS) was used to measure the severity of pains. The pains were measured on four occasions (before the intervention and on one-, two-, and three-month follow-ups). Data were analyzed using the t-test, chi-square, repeated measures ANOVA, and ANCOVA by SPSS 16. The overall severity of pain reduced from 6.51 ± 0.86 to 1.83 ± 1.25 in the intervention group ( p < 0.05). It increased from 6.41 ± 1.12 to 6.65 ± 1.37 in the control group ( p = 0.02). The repeated measures ANOVA showed that there is a significant difference in the change of pain scores between intervention and control groups ( p < 0.001, np2 = 0.572). Garlic extract can reduce pelvic and back pain, dysmenorrhea, and dyspareunia which are important symptoms of endometriosis.

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Rusandi, M. Arli, Dwi Yuwono Puji Sugiharto, and Sunawan Sunawan. "Effectiveness of solution-focused group counseling to improve planned happenstance skills of student." Jurnal Konseling dan Pendidikan 7, no. 1 (May 30, 2019): 30. http://dx.doi.org/10.29210/127600.

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This study aimed to determine the effectiveness of solution-focused group counseling to improve students planned happenstance skills. The experimental research method way repeated measures with pretest design and multiple posttest design, involving 5 students as participants which from 24 upperclassman of the guidance and counseling FKIP ULM which have low planned happenstance skills. The result of the research by using one-way ANOVA for repeated measures test showed that solution-focused group counseling was effective in improving the planned happenstance skills (F (2,8) = 325,895; p<0,001).

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Baghurst, Timothy, and Inza Fort. "Subjective Judging and the Home Advantage in Female Collegiate Division I Gymnastics." Women in Sport and Physical Activity Journal 17, no. 2 (October 2008): 3–7. http://dx.doi.org/10.1123/wspaj.17.2.3.

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The purpose of this study was to investigate the home advantage in female collegiate Division I gymnastics by apparatus and determine the performance effect of the Judges’ Assignor System (JAS) introduced in 2005 on each apparatus. Participant teams (N = 15) were selected based on their ranking in the top 25 nationally at the end of each regular season from 2003 to 2007. A repeated measures analysis of variance (ANOVA) revealed home scores for each apparatus were significantly higher than their respective away scores, with the largest differences occurring in the uneven bars and floor exercise. Additionally, a repeated measures ANOVA to assess the JAS impact on scores revealed that home performances yielded higher scores than away for all apparatus, and scores for all apparatus were lower both at home and away since the introduction of JAS. Results are assessed based on current research, and application for judges and coaches is discussed.

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Pastore, Donna L., and William G. Meacci. "Employment Process for NCAA Female Coaches." Journal of Sport Management 8, no. 2 (May 1994): 115–28. http://dx.doi.org/10.1123/jsm.8.2.115.

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This study examined the process by which female coaches of NCAA Division I, II, and III institutions are recruited, selected, and retained. A total of 501 administrators and coaches of women's teams responded to a questionnaire consisting of 22 employment process statements that elicited (a) the extent to which each process was used, and (b) the importance attached to each process by the subjects. Principal component analyses of the two data sets yielded five factors: Organizational Policies, Candidate's Experience, Informal Recruiting, Formal Recruiting, and Candidate's Credentials. A 2 × 2 × 3 × 5 (Gender × Position × Division × Use of Factors) repeated measures ANOVA showed significant two-way interaction effects for gender, position, and division. Tukey's post hoc analyses indicated that Candidate's Credentials was rated highest by all subgroups. A 2 × 2 × 3 × 5 (Gender × Position × Division × Importance of Factors) repeated measures ANOVA showed a significant two-way interaction effect for division. Tukey's post hoc analyses indicated that respondents rated Candidate's Credentials and Organizational Policies highest.

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Mateos-Salgado, Erik Leonardo, and Fructuoso Ayala-Guerrero. "Detection of electroencephalographic, electromyographic, and cardiac variations during wake-sleep transition through change point analysis." Salud mental 41, no. 1 (February 28, 2018): 17–23. http://dx.doi.org/10.17711/sm.0185-3325.2018.004.

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Introduction. Wake-sleep transition is a continuous, gradual process of change. Most studies evaluating electroencephalogram spectral power during this transition have used variance analysis (ANOVA). However, using this type of analysis does not allow one to detect specific changes in the statistical properties of a time series. Objective. To determine whether change point analysis (CPA) makes it possible to identify and characterize electroencephalographic, electromyographic, and cardiac changes during the wake-sleep transition through a cross-sectional study. Method. The study included 18 healthy volunteers (12 women and six men), from which polysomnography data were obtained during a two-minute transition. Heart rate, respiratory sinus arrhythmia, electroencephalogram spectral power, as well as electromyographic median and mean frequency and electromyographic root mean square were calculated in five-second segments. These segments were analyzed using repeated measures ANOVA, and CPA focused individually and for the group as a whole. Results. Repeated measures ANOVA and CPA by group found decreased levels of alpha and beta power and beta/delta index during wakefulness, and increased theta and delta power levels during sleep. CPA by individual found that only alpha power changed in all participants and failed to identify a specific moment when all the variables studied changed simultaneously. Discussion and conclusion. We consider that CPA provides additional information to statistical analyses such as ANOVA for the specific location of physiological changes during sleep-wake transition.

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Dadoun, F., P. Darmon, V. Achard, S. Boullu-Ciocca, F. Philip-Joet, M. C. Alessi, M. Rey, M. Grino, and A. Dutour. "Effect of sleep apnea syndrome on the circadian profile of cortisol in obese men." American Journal of Physiology-Endocrinology and Metabolism 293, no. 2 (August 2007): E466—E474. http://dx.doi.org/10.1152/ajpendo.00126.2007.

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It has been hypothesized that sleep apnea syndrome (SAS) increases hypothalamic-pituitary-adrenal axis activity and, through increased cortisol levels, participates in the pathophysiology of metabolic and cardiovascular complications. We compared the circadian profiles of cortisol in obese men with [obSAS+; apnea-hypopnea index (AHI) ≥ 20/h] and without SAS (obSAS−; AHI ≤ 5/h). 1) Salivary cortisol (5 samples: before/30 min after dinner, 2100, upon/30 min after awakening) was measured in 15 obSAS+, 19 obSAS−, and 19 normal-weight controls (NWC). 2) Plasma cortisol (every 30 min for 24 h under highly controlled conditions and portable EEG device) was measured in 9 obSAS+, 8 obSAS−, and 10 NWC men. Visceral adipose tissue surface was measured by CT scan. In both studies, obSAS+ and obSAS− men were comparable for age, BMI, waist circumference, and waist-to-hip ratio. First, no difference was found, using ANOVA for repeated measures, between obSAS+ and obSAS− subjects for any salivary cortisol measurement. No correlation was found between salivary cortisol and AHI or nocturnal SaO2. Similarly, obSAS+ and obSAS− men showed no difference in plasma cortisol rhythmicity: 24-h minimum, maximum, and mean, ANOVA for repeated measures, mathematical modeling of cortisol rhythm (COSINOR), and morning secretory peak. Conversely, ANOVA for repeated measures showed decreased cortisol levels in obese vs. NWC men during both the trough (2200–0130) and the peak (0600–0900) independently of SAS status. We show that SAS per se is not associated with any change of the level or of the features of salivary and plasma cortisol rhythmicity and confirm that men with visceral obesity display lower plasma cortisol levels than NWC men.

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Wright, Thomas A. "A Comparison Of Difference Score And Pre-Test Post-Test Repeated Measures ANOVA: Implications For Research In Management Information Systems." Journal of Applied Business Research (JABR) 6, no. 4 (October 21, 2011): 88. http://dx.doi.org/10.19030/jabr.v6i4.6279.

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Various forms of the pretest-posttest design are extensively used in Management Information Systems (MIS) research. There is a widespread misconception among MIS researchers regarding the equivalence of the difference score and pretest-posttest repeated measures ANOVA. Several important implications of the equivalence are presented which concern the interpretation of interaction effects, the test of normality, the test of equality of variance, and the experimentwise Type I error rate.

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Karageorghis, Costas I., Denis A. Mouzourides, David-Lee Priest, Tariq A. Sasso, Daley J. Morrish, and Carolyn L. Walley. "Psychophysical and Ergogenic Effects of Synchronous Music during Treadmill Walking." Journal of Sport and Exercise Psychology 31, no. 1 (February 2009): 18–36. http://dx.doi.org/10.1123/jsep.31.1.18.

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The present study examined the impact of motivational music and oudeterous (neutral in terms of motivational qualities) music on endurance and a range of psychophysical indices during a treadmill walking task. Experimental participants (N = 30; mean age = 20.5 years, SD = 1.0 years) selected a program of either pop or rock tracks from artists identified in an earlier survey. They walked to exhaustion, starting at 75% maximal heart rate reserve, under conditions of motivational synchronous music, oudeterous synchronous music, and a no-music control. Dependent measures included time to exhaustion, ratings of perceived exertion (RPE), and in-task affect (both recorded at 2-min intervals), and exercise-induced feeling states. A one-way repeated measures ANOVA was used to analyze time to exhaustion data. Two-way repeated measures (Music Condition × Trial Point) ANOVAs were used to analyze in-task measures, whereas a one-way repeated measures MANOVA was used to analyze the exercise-induced feeling states data. Results indicated that endurance was increased in both music conditions and that motivational music had a greater ergogenic effect than did oudeterous music (p < .01). In addition, in-task affect was enhanced by motivational synchronous music when compared with control throughout the trial (p < .01). The experimental conditions did not impact significantly (p > .05) upon RPE or exercise-induced feeling states, although a moderate effect size was recorded for the latter (ηp2 = .09). The present results indicate that motivational synchronous music can elicit an ergogenic effect and enhance in-task affect during an exhaustive endurance task.

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Smith, LL, SJ Semple, AJ McKune, N. Neveling, M. Caldeira, J.-M. Swanepoel, L. Tsomele, M. Naidoo, and E. Sibanda. "Changes in neutrophil count, creatine kinases and muscle soreness after repeated bouts of downhill running." South African Journal of Sports Medicine 19, no. 3 (October 5, 2007): 86. http://dx.doi.org/10.17159/2078-516x/2007/v19i3a260.

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Objective. A primary objective was to examine circulating neutrophil count after repeated bouts of downhill running. An additional aim was to determine creatine kinase (CK) levels during the initial 12 hours, after repeated DHRs. Design. Eleven healthy, untrained Caucasian males performed 2 x 60 min bouts of DHR (-13.5%), spaced 14 days apart, at a speed equal to 75% VO2max on a level grade. Blood was collected before, after, and every hour for 12 hours, and every 24 hours for 6 days. Absolute neutrophil count, CK, and delayed-onset muscle soreness (DOMS) were assessed. Results were analysed using repeated measures ANOVA (p

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O’Connell, Dennis G., Kimberly A. Cooper, Shelby M. Richeson, Mathilde B. Moeller, Jason W. Stephens, and DeAndre J. Key. "The effect of forearm position on elbow flexion strength in nursing, occupational, and physical therapy students." Work 69, no. 2 (June 24, 2021): 403–9. http://dx.doi.org/10.3233/wor-213486.

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BACKGROUND: Novice nurses, occupational and physical therapist’s injury rates are alarming. OBJECTIVE: To test for differences in peak elbow flexion forces (PEFF) by profession using different forearm positions. METHODS: Entry-level RN, OT, and PT students performed 3-repetitions of standing PEFF in forearm supination, pronation, and neutral. A one-way repeated measures ANOVA determined the forearm position with the greatest PEFF. A one-way ANOVA assessed differences in PEFF between professions. The alpha level was set at p≤0.05 for all analyses. RESULTS: Thirty 30 RN, 25 OT, and 30 PT students (x = 23.27 + /–3.29 yrs.) were studied. A one-way repeated measures ANOVA revealed a significant difference in PEFF between positions (F(2,168) = 144.3, p < 0.0001). A significant (p < 0.0001) pairwise comparison revealed neutral produced the greatest (28.15 + /–12.64 kg) and pronation the least PEFF (17.27 + /–7.40). PEFF was significantly different between position by profession (supination: F(2,82) = 10.14, p < 0.0001; pronation: F(2,82) = 10.33, p < 0.0001; neutral: F(2,82) = 13.39, p < 0.0001). PTs were significantly stronger than OTs and RN students in all forearm positions (p < 0.01). CONCLUSIONS: Neutral PEFF was greatest and PT students demonstrated greater PEFF than OT and RN students.

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Journal articles on the topic 'Repeated-measures ANOVA'

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