Reversion Toward the Mean Independently of Regression Toward the Mean

Methodology ◽  
2009 ◽  
Vol 5 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Merton S. Krause
Keyword(s):  
Random Error ◽  
Evaluation Studies ◽  
Internal Validity ◽  
Intervention Evaluation ◽  
Population Mean ◽  
Experimental Intervention ◽  
Sample Data ◽  
The Mean ◽  
Regression Toward The Mean

There is another important artifactual contributor to the apparent improvement of persons subjected to an experimental intervention which may be mistaken for regression toward the mean. This is the phenomenon of random error and extreme selection, which does not at all involve the population regression of posttest on pretest scores but involves a quite different and independent reversion of subjects’ scores toward the population mean. These two independent threats to the internal validity of intervention evaluation studies, however, can be detected and differentiated on the sample data of such studies.

Social Desirability Bias in Diary Panels is Evident in Panelists' Behavioral Frequency

2006 ◽  
Vol 99 (2) ◽  
pp. 322-334 ◽  
Author(s):  
Rex S. Toh ◽  
Eunkyu Lee ◽  
Michael Y. Hu
Keyword(s):  
Social Desirability ◽  
Large Scale ◽  
National Sample ◽  
Social Desirability Bias ◽  
Population Mean ◽  
Coefficients Of Variation ◽  
Behavioral Frequency ◽  
The Mean ◽  
Regression Toward The Mean ◽  
Behavioral Frequencies

The purpose was to analyze a large-scale database, a national sample of 1,108 heads of households collected by AT&T, to show that behavioral frequencies of the activities of consumer diary panelists may regress toward the population mean during the diary-keeping period given social desirability bias produced by the conditioning effect of keeping diaries. This effect is distinguished from regression toward the mean, a statistical phenomenon reflecting happenstance of extreme initial values. Social desirability bias is demonstrated in two ways—by observing decreasing coefficients of variation over time and by detecting a greater proportion of panelists' behavioral frequencies moving toward the population mean than moving away from it. Both cannot be explained by regression toward the mean. Social desirability bias was manifest only during the early stages of the diary-keeping period and only for activities high in involvement. The presence of social desirability bias in diary panels implies that, when people are subjected to observation, diary observations may be contaminated, leading to the mistaken impression that the population is more homogeneous than it actually is. Thus it is important for researchers to monitor the diary panel carefully to detect social desirability bias when engaged in longitudinal studies.

scholarly journals Understanding the Difference Between Standard Deviation and Standard Error of the Mean, and Knowing When to Use Which

2020 ◽  
Vol 42 (4) ◽  
pp. 409-410
Author(s):  
Chittaranjan Andrade
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Standard Error ◽  
Statistical Significance ◽  
Sample Mean ◽  
Population Mean ◽  
Sample Data ◽  
The Mean ◽  
The Difference

Many authors are unsure of whether to present the mean along with the standard deviation (SD) or along with the standard error of the mean (SEM). The SD is a descriptive statistic that estimates the scatter of values around the sample mean; hence, the SD describes the sample. In contrast, the SEM is an estimate of how close the sample mean is to the population mean; it is an intermediate term in the calculation of the 95% confidence interval around the mean, and (where applicable) statistical significance; the SEM does not describe the sample. Therefore, the mean should always be accompanied by the SD when describing the sample. There are many reasons why the SEM continues to be reported, and it is argued that none of these is justifiable. In fact, presentation of SEMs may mislead readers into believing that the sample data are more precise than they actually are. Given that the standard error is not presented for other parameters, such as difference between means or proportions, and difference between proportions, it is suggested that presentation of SEM values can be done away with, altogether.

scholarly journals Neutrosophic ratio-type estimators for estimating the population mean

2021 ◽  
Author(s):  
Zaigham Tahir ◽  
Hina Khan ◽  
Muhammad Aslam ◽  
Javid Shabbir ◽  
Yasar Mahmood ◽  
...  
Keyword(s):  
Minimum Mean Square Error ◽  
Auxiliary Information ◽  
Population Parameter ◽  
Uncertain Information ◽  
Population Mean ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
The Mean ◽  
Type Data ◽  
First Time

AbstractAll researches, under classical statistics, are based on determinate, crisp data to estimate the mean of the population when auxiliary information is available. Such estimates often are biased. The goal is to find the best estimates for the unknown value of the population mean with minimum mean square error (MSE). The neutrosophic statistics, generalization of classical statistics tackles vague, indeterminate, uncertain information. Thus, for the first time under neutrosophic statistics, to overcome the issues of estimation of the population mean of neutrosophic data, we have developed the neutrosophic ratio-type estimators for estimating the mean of the finite population utilizing auxiliary information. The neutrosophic observation is of the form $${Z}_{N}={Z}_{L}+{Z}_{U}{I}_{N}\, {\rm where}\, {I}_{N}\in \left[{I}_{L}, {I}_{U}\right], {Z}_{N}\in [{Z}_{l}, {Z}_{u}]$$ Z N = Z L + Z U I N where I N ∈ I L , I U , Z N ∈ [ Z l , Z u ] . The proposed estimators are very helpful to compute results when dealing with ambiguous, vague, and neutrosophic-type data. The results of these estimators are not single-valued but provide an interval form in which our population parameter may have more chance to lie. It increases the efficiency of the estimators, since we have an estimated interval that contains the unknown value of the population mean provided a minimum MSE. The efficiency of the proposed neutrosophic ratio-type estimators is also discussed using neutrosophic data of temperature and also by using simulation. A comparison is also conducted to illustrate the usefulness of Neutrosophic Ratio-type estimators over the classical estimators.

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

Comment on “Three Aspects of Cognitive Development”

1978 ◽  
Vol 46 (1) ◽  
pp. 129-130 ◽  
Author(s):  
William Asher
Keyword(s):  
Cognitive Development ◽  
Experimental Design ◽  
Age Groups ◽  
Quasi Experimental ◽  
The Mean ◽  
Regression Toward The Mean ◽  
Two Age Groups

In Exp. 2 of Siegler's 1976 study a faulty quasi-experimental design was used. The stated results, that older and younger children with apparently equal initial performance derived different benefits from identical experience, can also be explained by a confounding with regression toward the mean. These effects are a result of a selective matching of subjects from two age groups on a fallible variable correlated with age.

Markov Chains and Regression toward the Mean

1991 ◽  
Vol 26 (1) ◽  
pp. 115-125 ◽  
Author(s):  
Robert W. Kolb ◽  
Ricardo J. Rodriguez
Keyword(s):  
Markov Chains ◽  
The Mean ◽  
Regression Toward The Mean

Eating behaviour of male racing jockeys

1987 ◽  
Vol 17 (1) ◽  
pp. 249-253 ◽  
Author(s):  
Michael B. King ◽  
Gillian Mezey
Keyword(s):  
Eating Disorders ◽  
Response Rate ◽  
Eating Behaviour ◽  
Eating Attitudes ◽  
Eating Attitudes Test ◽  
Food Avoidance ◽  
Population Mean ◽  
Appetite Suppressants ◽  
The Mean ◽  
The Relationship

SynopsisJockeys based in racing stables in the Newmarket and Epsom areas of England were screened using the 26-item Eating Attitudes Test. The mean score of the EAT was 14·9, significantly above that reported for males in other studies. The response rate was poor, but 10 jockeys agreed to a full psychiatric and eating interview. The majority of jockeys interviewed reported food avoidance, the use of saunas, and the abuse of laxatives. Diuretics and appetite suppressants were also used. Bingeing was common, but self-induced vomiting was unusual. Current weights were 13% below, and the lowest reported weights 21% below, matched population mean weights. The relationship between these forms of eating behaviour and clinical eating disorders is discussed.

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