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Book ChapterDOI

Design and Analysis

About: The article was published on 2010-01-01. It has received 714 citations till now.
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Journal ArticleDOI
TL;DR: Replication is one of the most important tools for the verification of facts within the empirical sciences as discussed by the authors, and a detailed examination of the notion of replication reveals that there are many different...
Abstract: Replication is one of the most important tools for the verification of facts within the empirical sciences. A detailed examination of the notion of replication reveals that there are many different...

803 citations


Cites background from "Design and Analysis"

  • ...Other authors have proposed similar or slightly different conceptions such as exact, partial, and conceptual replication (Hendrick, 1991), concrete and conceptual replication (Sargent, 1981), or exact and inexact replication (Keppel, 1982)....

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Journal ArticleDOI
TL;DR: Key applications and the main phenomena related to acoustic propagation are summarized, and how they affect the design and operation of communication systems and networking protocols at various layers are discussed.
Abstract: This paper examines the main approaches and challenges in the design and implementation of underwater wireless sensor networks. We summarize key applications and the main phenomena related to acoustic propagation, and discuss how they affect the design and operation of communication systems and networking protocols at various layers. We also provide an overview of communications hardware, testbeds and simulation tools available to the research community.

728 citations


Cites background from "Design and Analysis"

  • ...Although long propagation still causes inefficiency, synchronization allows protocols to exploit the space-time volume, intentionally overlapping packets in time while they remain distinct in space (Ahn et al., 2011)....

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  • ...In fact, while ALOHA is rarely considered in radio systems due to its poor throughput, it is a potential candidate for underwater networks when combined with simple CSMA features (Ahn et al., 2011)....

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Journal ArticleDOI
TL;DR: This study provides a systematic examination of F‐test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences.
Abstract: BACKGROUND: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. METHOD: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. RESULTS: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions. // Antecedentes: las consecuencias de la violacion de la normalidad sobre la robustez del estadistico F han sido estudiadas desde 1930 y siguen siendo de interes en la actualidad. Sin embargo, aunque la investigacion ha sido extensa, los resultados son contradictorios, encontrandose evidencia a favor y en contra de su robustez. El presente estudio presenta un analisis sistematico de la robustez del estadistico F en terminos de error de Tipo I ante violaciones de la normalidad, considerando una amplia variedad de distribuciones frecuentemente encontradas en ciencias sociales y de la salud. METODO: se ha realizado un estudio de simulacion Monte Carlo considerando un diseno de tres grupos y diferentes distribuciones conocidas y no conocidas. Las variables manipuladas han sido: igualdad o desigualdad del tamano de los grupos, tamano muestral total y de los grupos; coeficiente de variacion del tamano muestral; forma de la distribucion e igualdad o desigualdad de la forma en los grupos; y emparejamiento entre el tamano muestral con el grado de contaminacion en la distribucion. RESULTADOS: los resultados muestran que el estadistico F es robusto en terminos de error de Tipo I en el 100% de los casos estudiados, independientemente de las condiciones manipuladas.

721 citations


Cites background or result from "Design and Analysis"

  • ...…the conditions studied here, with those classical handbooks which conclude that F-test is only robust if the departure from normality is moderate (Keppel, 1982; Montgomery, 1991), the populations have the same distributional shape (Kirk, 2013), and the sample sizes are large and equal (Winer et…...

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  • ...Based on most early studies, many classical handbooks on research methods in education and psychology draw the following conclusions: Moderate departures from normality are of little concern in the fi xed-effects analysis of variance (Montgomery, 1991); violations of normality do not constitute a serious problem, unless the violations are especially severe (Keppel, 1982); F-test is robust to moderate departures from normality when sample sizes are reasonably large and are equal (Winer, Brown, & Michels, 1991); and researchers do not need to be concerned about moderate departures from normality provided that the populations are homogeneous in form (Kirk, 2013)....

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  • ...By contrast, however, our results do not concur, at least for the conditions studied here, with those classical handbooks which conclude that F-test is only robust if the departure from normality is moderate (Keppel, 1982; Montgomery, 1991), the populations have the same distributional shape (Kirk, 2013), and the sample sizes are large and equal (Winer et al....

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  • ...…analysis of variance (Montgomery, 1991); violations of normality do not constitute a serious problem, unless the violations are especially severe (Keppel, 1982); F-test is robust to moderate departures from normality when sample sizes are reasonably large and are equal (Winer, Brown, & Michels,…...

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Journal ArticleDOI
TL;DR: Among patients with stable angina, both thosetreated with PCI and those treated with optimal medical therapy alone had marked improvements in health status during follow-up, and similar incremental benefits from PCI were seen in some but not all RAND-36 domains.
Abstract: At baseline, 22% of the patients were free of angina. At 3 months, 53% of the patients in the PCI group and 42% in the medical-therapy group were angina-free (P<0.001). Baseline mean (±SD) Seattle Angina Questionnaire scores (which range from 0 to 100, with higher scores indicating better health status) were 66±25 for physical limitations, 54±32 for angina stability, 69±26 for angina frequency, 87±16 for treatment satisfaction, and 51±25 for quality of life. By 3 months, these scores had increased in the PCI group, as compared with the medical-therapy group, to 76±24 versus 72±23 for physical limitation (P = 0.004), 77±28 versus 73±27 for angina stability (P = 0.002), 85±22 versus 80±23 for angina frequency (P<0.001), 92±12 versus 90±14 for treatment satisfaction (P<0.001), and 73±22 versus 68±23 for quality of life (P<0.001). In general, patients had an incremental benefit from PCI for 6 to 24 months; patients with more severe angina had a greater benefit from PCI. Similar incremental benefits from PCI were seen in some but not all RAND-36 domains. By 36 months, there was no significant difference in health status between the treatment groups. Conclusions Among patients with stable angina, both those treated with PCI and those treated with optimal medical therapy alone had marked improvements in health status during follow-up. The PCI group had small, but significant, incremental benefits that disappeared by 36 months. (ClinicalTrials.gov number, NCT00007657.)

656 citations

Journal ArticleDOI
TL;DR: This review contends that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status, and provides evidence that measures derived from 5-min recordings of resting and submaximal exercise heart rate are likely the most useful monitoring tools.
Abstract: Monitoring an athlete's physiological status in response to various types and volumes of (aerobic-oriented) training can provide useful information for optimizing training programs. Measures of resting, exercise and recovery heart rate (HR) are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load 1) daily during specific training blocks and 2) throughout the competitive season. These measures are still not widely implemented to monitor athletes’ responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in HR, reflecting parasympathetic activity) and submaximal exercise (30- to 60-s average) HR are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker’s sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of HR cannot inform on all aspects of wellness, fatigue and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status

590 citations


Cites background from "Design and Analysis"

  • ...3 × CV performance improvement may give a top athlete one extra medal every ten races (Hopkins et al., 1999)....

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  • ...Simulations have shown that such a 0.3 × CV performance improvement may give a top athlete one extra medal every ten races (Hopkins et al., 1999)....

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References
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Book
01 Dec 1969
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Abstract: Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That a Proportion is .50 and the Sign Test. Differences Between Proportions. Chi-Square Tests for Goodness of Fit and Contingency Tables. The Analysis of Variance and Covariance. Multiple Regression and Correlation Analysis. Set Correlation and Multivariate Methods. Some Issues in Power Analysis. Computational Procedures.

115,069 citations

Journal ArticleDOI
TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

80,095 citations

Book
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs

53,113 citations

Journal ArticleDOI
TL;DR: The CES-D scale as discussed by the authors is a short self-report scale designed to measure depressive symptomatology in the general population, which has been used in household interview surveys and in psychiatric settings.
Abstract: The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.

48,339 citations