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Cheng, Siwei – Sociological Methods & Research, 2023
One of the most important developments in the current era of social sciences is the growing availability and diversity of data, big and small. Social scientists increasingly combine information from multiple data sets in their research. While conducting statistical analyses with linked data is relatively straightforward, borrowing information…
Descriptors: Social Science Research, Statistical Analysis, Statistical Distributions, Statistical Bias
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Poom, Leo; af Wåhlberg, Anders – Research Synthesis Methods, 2022
In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the…
Descriptors: Meta Analysis, Effect Size, Mathematical Formulas, Monte Carlo Methods
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Liu, Xiaofeng Steven; Shin, Hyejo Hailey – Teaching Statistics: An International Journal for Teachers, 2020
Computer simulation can be used to demonstrate why the unbiased sample variance uses degrees of freedom (n-1). This is first demonstrated for sampling from a normal random variable, and in additional simulations for some selected non-normal random variables, namely, chi-square and binomial.
Descriptors: Computer Simulation, Statistics, Sampling, Statistical Bias
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White, Simon R.; Bonnett, Laura J. – Teaching Statistics: An International Journal for Teachers, 2019
The statistical concept of sampling is often given little direct attention, typically reduced to the mantra "take a random sample". This low resource and adaptable activity demonstrates sampling and explores issues that arise due to biased sampling.
Descriptors: Statistical Bias, Sampling, Statistical Analysis, Learning Activities
Ke, Zijun; Zhang, Zhiyong – Grantee Submission, 2018
Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such…
Descriptors: Correlation, Mathematical Formulas, Sampling, Monte Carlo Methods
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Tarray, Tanveer A.; Singh, Housila P.; Yan, Zaizai – Sociological Methods & Research, 2017
This article addresses the problem of estimating the proportion Pi[subscript S] of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is…
Descriptors: Models, Efficiency, Sampling, Research Problems
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Suero, Manuel; Privado, Jesús; Botella, Juan – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
A simulation study is presented to evaluate and compare three methods to estimate the variance of the estimates of the parameters d and "C" of the signal detection theory (SDT). Several methods have been proposed to calculate the variance of their estimators, "d'" and "c." Those methods have been mostly assessed by…
Descriptors: Evaluation Methods, Theories, Simulation, Statistical Analysis
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Maeda, Hotaka; Zhang, Bo – International Journal of Testing, 2017
The omega (?) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected…
Descriptors: Cheating, Test Items, Mathematics, Statistics
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Bishara, Anthony J.; Hittner, James B. – Educational and Psychological Measurement, 2015
It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…
Descriptors: Research Methodology, Monte Carlo Methods, Correlation, Simulation
Tran, Dung; Lee, Hollylynne; Doerr, Helen – Mathematics Education Research Group of Australasia, 2016
The research reported here uses a pre/post-test model and stimulated recall interviews to assess teachers' statistical reasoning about comparing distributions, when enrolled in a graduate-level statistics education course. We discuss key aspects of the course design aimed at improving teachers' learning and teaching of statistics, and the…
Descriptors: Faculty Development, Thinking Skills, Graduate Students, Statistics
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Ugille, Maaike; Moeyaert, Mariola; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim – Journal of Experimental Education, 2014
A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect…
Descriptors: Effect Size, Statistical Bias, Sample Size, Regression (Statistics)
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Reardon, Sean F.; Unlu, Fatih; Zhu, Pei; Bloom, Howard S. – Journal of Educational and Behavioral Statistics, 2014
We explore the use of instrumental variables (IV) analysis with a multisite randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, an assumption known in the IV literature as the exclusion restriction.…
Descriptors: Statistical Bias, Statistical Analysis, Least Squares Statistics, Sampling
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Hansen, Henrik; Klejnstrup, Ninja Ritter; Andersen, Ole Winckler – American Journal of Evaluation, 2013
There is a long-standing debate as to whether nonexperimental estimators of causal effects of social programs can overcome selection bias. Most existing reviews either are inconclusive or point to significant selection biases in nonexperimental studies. However, many of the reviews, the so-called "between-studies," do not make direct…
Descriptors: Foreign Countries, Developing Nations, Outcome Measures, Comparative Analysis
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Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
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Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2013
Osborne and Waters (2002) focused on checking some of the assumptions of multiple linear regression. In a critique of that paper, Williams, Grajales, and Kurkiewicz correctly clarify that regression models estimated using ordinary least squares require the assumption of normally distributed errors, but not the assumption of normally distributed…
Descriptors: Multiple Regression Analysis, Least Squares Statistics, Computation, Statistical Analysis
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