scholarly journals Assessment of the Randomization Test for Binomial Sex-Ratio Distributions in Birds

The Auk ◽  
2003 ◽  
Vol 120 (1) ◽  
pp. 62-68
Author(s):  
John G. Ewen ◽  
Phillip Cassey ◽  
Robert A. R. King
Keyword(s):  
Sex Ratio ◽  
Binomial Distribution ◽  
Type I Error ◽  
Analytical Techniques ◽  
Randomization Test ◽  
Entire Population ◽  
Type I ◽  
Primary Sex Ratio ◽  
Binomial Distributions ◽  
Common Distribution

Abstract We assessed a randomization test frequently used in studies that aim to detect bias in primary sex ratio of avian species. Three different treatments were examined that represent simple but ecologically realistic cases of interest to researchers. The randomization test was successful in reducing Type I error when testing for a significant departure from a single binomial distribution. When brood sizes or sample sizes were low, however, the randomization test lacked power to detect departures from a population of broods with multiple binomial distributions of sons and daughters. We recommend analytical techniques available to researchers that do not require a common distribution of the sexes to broods for an entire population.

scholarly journals An Improved Two Independent-Samples Randomization Test for Single-Case AB-Type Intervention Designs: A 20-Year Journey

2020 ◽  
Vol 18 (1) ◽  
pp. 2-20
Author(s):  
Joel R. Levin ◽  
John M. Ferron ◽  
Boris S. Gafurov
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Single Case ◽  
Test Procedure ◽  
Randomization Test ◽  
Multiple Baseline ◽  
Type I ◽  
Two Phase ◽  
Sample Test ◽  
Adequate Power

Detailed is a 20-year arduous journey to develop a statistically viable two-phase (AB) single-case two independent-samples randomization test procedure. The test is designed to compare the effectiveness of two different interventions that are randomly assigned to cases. In contrast to the unsatisfactory simulation results produced by an earlier proposed randomization test, the present test consistently exhibited acceptable Type I error control under various design and effect-type configurations, while at the same time possessing adequate power to detect moderately sized intervention-difference effects. Selected issues, applications, and a multiple-baseline extension of the two-sample test are discussed.

scholarly journals Identifying Which of J Independent Binomial Distributions Has the Largest Probability of Success

2020 ◽  
Vol 18 (2) ◽  
pp. 2-9
Author(s):  
Rand Wilcox
Keyword(s):  
Binomial Distribution ◽  
Random Variables ◽  
Independent Random Variables ◽  
Pairwise Comparisons ◽  
Type I ◽  
Type I Errors ◽  
Binomial Distributions ◽  
Probability Of Success

Let p1,…, pJ denote the probability of a success for J independent random variables having a binomial distribution and let p(1) ≤ … ≤ p(J) denote these probabilities written in ascending order. The goal is to make a decision about which group has the largest probability of a success, p(J). Let p̂1,…, p̂J denote estimates of p1,…,pJ, respectively. The strategy is to test J − 1 hypotheses comparing the group with the largest estimate to each of the J − 1 remaining groups. For each of these J − 1 hypotheses that are rejected, decide that the group corresponding to the largest estimate has the larger probability of success. This approach has a power advantage over simply performing all pairwise comparisons. However, the more obvious methods for controlling the probability of one more Type I errors perform poorly for the situation at hand. A method for dealing with this is described and illustrated.

GEOGRAPHIC CLUSTERING OF THE SECONDARY SEX RATIO IN JAPAN: ASSOCIATION WITH DEMOGRAPHIC ATTRIBUTES

2012 ◽  
Vol 45 (2) ◽  
pp. 279-284 ◽  
Author(s):  
YOSUKE INOUE ◽  
MASAHIRO UMEZAKI ◽  
CHIHO WATANABE
Keyword(s):  
Life Expectancy ◽  
Sex Ratio ◽  
Type I Error ◽  
Divorce Rate ◽  
Vital Statistics ◽  
Scan Statistics ◽  
Type I ◽  
Geographic Clustering ◽  
Secondary Sex Ratio ◽  
Demographic Attributes

SummaryThe secondary sex ratio (SSR) has been suggested to decrease with adverse physical and psychological environments. Previous studies have focused on reduced SSR under adverse conditions, such as war, terrorism attack and earthquake, but few studies have investigated fluctuations in SSR in moderately adverse environments. This study analysed municipality-level vital statistics records in Japan collected between 1998 and 2002 to identify high-SSR clusters and low-SSR clusters with spatial-scan statistics. In 999 runs of simulation, high- and low-SSR clusters were detected but fewer than 950 times, indicating that SSR was not geographically clustered in Japan if type I error of 5% was adopted. Explorative analyses comparing demographic attributes between high-SSR clusters and low-SSR clusters that were detected more than 500 times in 999 runs of simulation, showed that rate of spontaneous abortion, rate of artificial abortion and divorce rate were higher in low-SSR clusters, while male life expectancy, female life expectancy and total fertility rate were higher in a high-SSR cluster.

scholarly journals ISLAMIC MUTUAL FUNDS PROMOTIONAL ACTIVITIES: NORMAL APPROXIMATION OF BINOMIAL DISTRIBUTION AND TYPE I ERROR

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohd Yaziz Bin Mohd Isa
Keyword(s):  
Probability Distribution ◽  
Mutual Funds ◽  
Binomial Distribution ◽  
Null Hypothesis ◽  
Type I Error ◽  
Normal Approximation ◽  
Approximation Procedure ◽  
Type I ◽  
Binomial Probability ◽  
Population Mean

The paper applies normal approximation procedure to binomial probability distribution. A sample of 392 respondents are surveyed whether they agree or not agree that promotional activities determined the level of awareness of benefits of Islamic mutual funds. The paper hypothesizes the population mean µ of success effects of promotional activities at 68%, and attempts to reduce Type I error - the probability of rejecting null hypothesis when it is true.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

How to Detect Publication Bias in Psychological Research

2019 ◽  
Vol 227 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Frank Renkewitz ◽  
Melanie Keiner
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Statistical Power ◽  
Type I Error ◽  
Psychological Research ◽  
Type I ◽  
True Effect Size ◽  
Questionable Research Practices ◽  
True Effect ◽  
Meta Analyses

Abstract. Publication biases and questionable research practices are assumed to be two of the main causes of low replication rates. Both of these problems lead to severely inflated effect size estimates in meta-analyses. Methodologists have proposed a number of statistical tools to detect such bias in meta-analytic results. We present an evaluation of the performance of six of these tools. To assess the Type I error rate and the statistical power of these methods, we simulated a large variety of literatures that differed with regard to true effect size, heterogeneity, number of available primary studies, and sample sizes of these primary studies; furthermore, simulated studies were subjected to different degrees of publication bias. Our results show that across all simulated conditions, no method consistently outperformed the others. Additionally, all methods performed poorly when true effect sizes were heterogeneous or primary studies had a small chance of being published, irrespective of their results. This suggests that in many actual meta-analyses in psychology, bias will remain undiscovered no matter which detection method is used.

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