Sample Sizes for Observational Studies

2004 ◽  
pp. 709-727
Keyword(s):  
Observational Studies ◽  
Sample Sizes

Implications of Small Samples for Generalization: Adjustments and Rules of Thumb

2016 ◽  
Vol 41 (5) ◽  
pp. 472-505 ◽  
Author(s):  
Elizabeth Tipton ◽  
Kelly Hallberg ◽  
Larry V. Hedges ◽  
Wendy Chan
Keyword(s):  
Observational Studies ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Small Samples ◽  
Sample Sizes ◽  
Random Samples ◽  
Rules Of Thumb ◽  
Large Populations ◽  
Small Sample Sizes ◽  
Combine Information

Background: Policy makers and researchers are frequently interested in understanding how effective a particular intervention may be for a specific population. One approach is to assess the degree of similarity between the sample in an experiment and the population. Another approach is to combine information from the experiment and the population to estimate the population average treatment effect (PATE). Method: Several methods for assessing the similarity between a sample and population currently exist as well as methods estimating the PATE. In this article, we investigate properties of six of these methods and statistics in the small sample sizes common in education research (i.e., 10–70 sites), evaluating the utility of rules of thumb developed from observational studies in the generalization case. Result: In small random samples, large differences between the sample and population can arise simply by chance and many of the statistics commonly used in generalization are a function of both sample size and the number of covariates being compared. The rules of thumb developed in observational studies (which are commonly applied in generalization) are much too conservative given the small sample sizes found in generalization. Conclusion: This article implies that sharp inferences to large populations from small experiments are difficult even with probability sampling. Features of random samples should be kept in mind when evaluating the extent to which results from experiments conducted on nonrandom samples might generalize.

The association between dietary inflammatory index and risk of central obesity in adults: An updated systematic review and meta-analysis

2020 ◽  
Vol 90 (5-6) ◽  
pp. 535-552 ◽  
Author(s):  
Mahdieh Abbasalizad Farhangi ◽  
Mahdi Vajdi
Keyword(s):  
Systematic Review ◽  
Observational Studies ◽  
Central Obesity ◽  
Assessment Tool ◽  
Weighted Mean Difference ◽  
Meta Analysis ◽  
Dietary Factors ◽  
Cross Sectional ◽  
Inflammatory Status ◽  
Obesity Indices

Abstract. Backgrounds: Central obesity, as a pivotal component of metabolic syndrome is associated with numerous co-morbidities. Dietary factors influence central obesity by increased inflammatory status. However, recent studies didn’t evaluate the association between central obesity and dietary inflammation index (DII®) that give score to dietary factors according to their inflammatory potential. In the current systematic review and meta-analysis, we summarized the studies that investigated the association between DII® with central obesity indices in the general populations. Methods: In a systematic search from PubMed, SCOPUS, Web of Sciences and Cochrane electronic databases, we collected relevant studies written in English and published until 30 October 2019. The population of included studies were apparently healthy subjects or individuals with obesity or obesity-related diseases. Observational studies that evaluated the association between DII® and indices of central obesity including WC or WHR were included. Results: Totally thirty-two studies were included; thirty studies were cross-sectional and two were cohort studies with 103071 participants. Meta-analysis of observational studies showed that higher DII® scores were associated with 1.81 cm increase in WC (Pooled weighted mean difference (WMD) = 1.813; CI: 0.785–2.841; p = 0.001). Also, a non-significant increase in the odds of having higher WC (OR = 1.162; CI: 0.95–1.43; p = 0.154) in the highest DII category was also observed. In subgroup analysis, the continent, dietary assessment tool and gender were the heterogeneity sources. Conclusion: The findings proposed that adherence to diets with high DII® scores was associated with increased WC. Further studies with interventional designs are necessary to elucidate the causality inference between DII® and central obesity indices.

Is Red Really Romantic?

2017 ◽  
Vol 48 (3) ◽  
pp. 174-183 ◽  
Author(s):  
Gabrielle K. Lehmann ◽  
Robert J. Calin-Jageman
Keyword(s):  
Opposite Direction ◽  
Positive Control ◽  
Weak Effect ◽  
Sample Sizes ◽  
Quality Controls

Abstract. Red has been reported to enhance attraction for women rating men ( Elliot et al., 2010 ) and men rating women ( Elliot & Niesta, 2008 ). We replicated one of these studies online and in-person. To ensure rigor, we obtained original materials, planned for informative sample sizes, pre-registered our study, used a positive control, and adopted quality controls. For men, we found a very weak effect in the predicted direction (d = 0.09, 95% CI [−0.17, 0.34], N = 242). For women, we found a very weak effect in the opposite direction (d = −0.09, 95% CI [−0.30, 0.12], N = 360). The original studies may have overestimated the red effect, our studies may be an underestimate, or there could be strong moderation of the effect of red on attraction.

Norm-Block Sample Sizes: A Review of 15 Multidimensional Intelligence Tests

2013 ◽  
Author(s):  
Randy G. Floyd ◽  
Ryan L. Farmer ◽  
Sarah Irby ◽  
Phil Norfolk ◽  
Haley Hawkins ◽  
...  
Keyword(s):  
Intelligence Tests ◽  
Sample Sizes ◽  
Block Sample

On the Mathematical Basis of Zelen’s Prerandomized Designs

1985 ◽  
Vol 24 (03) ◽  
pp. 120-130 ◽  
Author(s):  
E. Brunner ◽  
N. Neumann
Keyword(s):  
Clinical Trial ◽  
Normal Distribution ◽  
Random Variables ◽  
Small Samples ◽  
Selection Effects ◽  
Sample Sizes ◽  
Mathematical Basis ◽  
Additional Assumptions

SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.

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