The Sample Size-Richness Relation: The Relevance of Research Questions, Sampling Strategies, and Behavioral Variation

1993 ◽  
Vol 58 (3) ◽  
pp. 489-496 ◽  
Author(s):  
Stephen Plog ◽  
Michelle Hegmon
Keyword(s):  
Sample Size ◽  
Strong Correlation ◽  
Sufficient Evidence ◽  
Sampling Strategies ◽  
Behavioral Factors ◽  
Behavioral Variation ◽  
Research Questions ◽  
Automatic Decisions

Recent discussions of the relation between sample size and assemblage richness suggest that researchers should control for that relation before behavioral interpretations of richness levels can proceed. We suggest that such automatic decisions should be avoided because a strong correlation between sample size and richness alone is not sufficient evidence for dismissing behavioral factors as the primary causes of the variation in richness. Instead, we must first explore the causes of the observed variation in sample size and the relation of those causes to our research questions.

Perceived Influence of Students’ Demographic Variables on Their Access to Financial Aids in Public Universities in Rivers State, Nigeria

2018 ◽  
Vol 8 (2) ◽  
pp. 32-50
Author(s):  
Emmanuel Olorunleke Eseyin
Keyword(s):  
Sample Size ◽  
Public Universities ◽  
Sampling Technique ◽  
Demographic Variables ◽  
Sample Size Determination ◽  
Interview Schedule ◽  
The Public ◽  
Research Questions ◽  
Level Of Significance ◽  
Rivers State

The paper investigated the perceived influence of students’ demographic variables on their access to financial aids in public Universities in Rivers State, Nigeria. Six questions were formulated to guide the study and five hypotheses tested at 0.05 level of significance. The design adopted for the study was an analytical survey. The population of the study included 78, 216 students (34,997 male and 43,219 female) in the three public Universities in Rivers State. The sample of the study covered 791 students (Male= 395 and Female= 396) selected through the random sampling technique while Taro Yamane method of sample size determination was used for determining the sample size. The instruments used for collecting responses from students were questionnaire and a ten items interview schedule. The research questions were answered using frequency, percentage and cumulative percentage. Findings of the study revealed that students’ demographic variables have an influence on their access to financial aids in public Universities in Rivers State, Nigeria. The implication of this is that the government’s expenditure on education will continue to increase in the absence of these alternative financial aids in the public Universities in Rivers State, Nigeria.

scholarly journals The 2 × 2 Achievement Goals in Sport and Physical Activity Contexts: A Meta-Analytic Test of Context, Gender, Culture, and Socioeconomic Status Differences and Analysis of Motivations, Regulations, Affect, Effort, and Physical Activity Correlates

2019 ◽  
Vol 10 (1) ◽  
pp. 173-205 ◽  
Author(s):  
Marc Lochbaum ◽  
Thaís Zanatta ◽  
Zişan Kazak
Keyword(s):  
Physical Activity ◽  
Socioeconomic Status ◽  
Physical Education ◽  
Sample Size ◽  
Achievement Goals ◽  
Gender Culture ◽  
Research Questions ◽  
Mastery Avoidance ◽  
Avoidance Goal ◽  
Mastery Approach

Approach-avoidance achievement goals are studied extensively in the context of competitive sports and physical activity, including leisure and physical education. Building upon past meta-analyses, the purpose of this quantitative review was to provide basic descriptive data, estimated means for testing of several research questions (i.e., context, gender, culture, and socioeconomic status), and meta-analyzing outcome correlates (i.e., self-determination constructs, affect, effort, and physical activity). A total of 116 studies up to 1 December 2018, met inclusion criteria. These 116 studies, totaling a sample size of 43,133 participants (M sample size = 347.85 + 359.36), from 22 countries with 92.7% of samples, are drawn from participants less than 30 years of mean age. From the 116 unique studies, nearly half (49.6%) were from a sport context and the rest from leisure-time physical activity (PA) (19.4%) and physical education (PE) (31.0%) contexts. A number of different analyses were conducted to examine our research questions. Support was found for several of our research questions: The mastery-approach goal was endorsed more than all the other goals, while sport participants endorsed the performance-approach goal more than PA and PE groups; females endorsed the mastery-avoidance goal more than males; more culturally individualistic countries endorsed the mastery-approach goal; and countries from lower socioeconomic and interdependent countries endorsed the mastery-avoidance goal than higher socioeconomic and independent countries. Concerning, the meta-analyzed correlates, most relationships were hypothesized through the performance-approach goal, and both avoidance goals appeared to be too similar in relationships with the correlates raising theoretical concerns. Overall, the mastery-approach goals had the most meaningful biased corrected effect size values (rc) with the outcome correlates, such as relative autonomy (0.47), intrinsic motivation (0.52), effort (0.40), positive affect (0.42), physical activity intent (0.38). Based on the present and past meta-analytic results, the 2 × 2 achievement goals as currently measured was questioned. Future research suggestions included fundamental questionnaire issues, the need for latent profile analysis or other more advanced statistics, and whether the 2 × 2 achievement goal framework is the most appropriate framework in physical activity contexts.

The Numbers Will Love You Back in Return—I Promise

2016 ◽  
Vol 11 (4) ◽  
pp. 551-554 ◽  
Author(s):  
Martin Buchheit
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Clinical Medicine ◽  
Statistical Significance ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Sport Science ◽  
Size Dependent ◽  
Research Questions ◽  
Per Se

The first sport-science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 y ago in the first issue of this journal. While debate continues, MBI is today well established in sport science and in other fields, particularly clinical medicine, where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null-hypothesis significance testing and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and, in turn, study conclusions are sample-size dependent, irrespective of the size of the effect; significance does not inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualization; and MBI is supported by spreadsheets freely available on the Internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.

scholarly journals The peril of adjusting for baseline when using change as a predictor

2019 ◽  
Author(s):  
Kimmo Sorjonen ◽  
Daniel Falkstedt ◽  
Bo Melin ◽  
Michael Ingre
Keyword(s):  
High Risk ◽  
Sample Size ◽  
Simulation Study ◽  
Strong Correlation ◽  
High Reliability ◽  
Large Sample Size ◽  
Present Simulation ◽  
Very High ◽  
Time Point

Some studies have analyzed the effect of a predictor measured at a later time point (X1), or of the X1-X0 difference, while adjusting for the predictor measured at baseline (X0), on some outcome Y of interest. The present simulation study shows that, if used to analyze the effect of change in X on Y, there is a high risk for this analysis to produce type 1-errors, especially with a strong correlation between true X and Y, when X0 and X1 are not measured with very high reliability, and with a large sample size. These problems are not encountered if analyzing the unadjusted effect of the X1-X0 difference on Y instead, and as this effect exhibits power on par with the adjusted effect it seems as the preferable method when using change between two measurement points as a predictor.

scholarly journals The Relationship of Behavior Factors with Helminthiasis Incident on Helminthiasis Infection Disease in Pregnant Mothers in Langkat Regency

2020 ◽  
Vol 2 (1) ◽  
pp. 327-342
Author(s):  
Rini Aswita ◽  
Fazidah Aguslina Siregar ◽  
Nurmaini
Keyword(s):  
Clinical Study ◽  
Pregnant Women ◽  
Sample Size ◽  
Common Type ◽  
Behavioral Factors ◽  
Study Population ◽  
Infection Disease ◽  
Relationship Of ◽  
The Relationship ◽  
Pregnant Mothers

The aim of the study is to find out the behavior factors helminthiasis Infection Disease in Pregnant Mothers. The sample size in this study was determined based on the sample size formula for testing the hypothesis of the proportion of one clinical study population of Skovland and Vatn (2007). The results of research on the influence of behavioral factors and environmental factors on helminthiasis in pregnant women in Langkat Regency in 2019 are Helminthiasis status. There are 175 pregnant women, 20 percent of pregnant women are infected with helminthiasis, and the most common type of worm is Ascaris lumbricoides. Behavioral factors. Behavioral factors have a significant influence on the incidence of helminthiasis in pregnant women, namely the habit of wearing footwear, the habit of washing hands with soap before eating and after defecation, the habit of consuming raw food and the habit of defecating in addition to toilet.

Quantitative Legal Research

2020 ◽  
pp. 383-466
Author(s):  
P. Ishwara Bhat
Keyword(s):  
Data Analysis ◽  
Sample Size ◽  
Quantitative Data ◽  
Policy Making ◽  
Statistical Data ◽  
Central Tendency ◽  
Legal Research ◽  
Political Implications ◽  
Research Questions ◽  
Law Commission

Study of statistical data becomes inevitable because of the far-reaching socio-economic dimensions, demographic factors, and political implications of law’s operation. Quantitative legal research (QLR) insists on scientific measurement of the phenomena and appropriate generalization based on data analysis. The growing importance of QLR can be found in the policy making and implementing function of legislature, judiciary, and administration, and in the works of the Law Commission, policy researchers, and legal academicians. Designing of QLR entails framing of research questions, hypothesis formulation, and testing of the hypothesis in light of the statistical data collected. The sample size should be statistically appropriate and collection, organisation, presentation, analysis, and interpretation of data in QLR needs to be systematic. Analysing quantitative data by focusing on proportion, central tendency, and deviation enables to observe trends.

An Anthropological Perspective on the Sample Size-Richness Relation: A Response to Leonard

1997 ◽  
Vol 62 (4) ◽  
pp. 717-718 ◽  
Author(s):  
Stephen Plog ◽  
Michelle Hegmon
Keyword(s):  
Sample Size ◽  
Size Effects ◽  
Research Process ◽  
Full Agreement ◽  
Anthropological Perspective ◽  
Behavioral Variation ◽  
Important Concern

We are in full agreement with Leonard, and many others, that the sample size-richness relation is an important concern for archaeologists, and we appreciate his evenhanded discussion of our paper. However, we disagree with Leonard regarding the point in the research process when variable prehistoric behavior can be used to explain differences in richness. Leonard and others argue that we must always begin studies of richness by controlling any observed relation to sample size; we can only consider prehistoric behavior as an explanation of richness after we account for sample size effects. In contrast, we suggest that following his recommendations will obscure aspects of behavioral variation that are important to understanding prehistoric societies. Considerations of prehistoric behavior must therefore be central in studies of richness.

Statistical Uncertainty Analyses of Void Fraction Predictions Using Two Different Sampling Strategies: Latin Hypercube and Simple Random Sampling

2010 ◽  
Author(s):  
Augusto Hernandez-Solis ◽  
Christian Ekberg ◽  
Arvid O¨dega˚rd Jensen ◽  
Christophe Demaziere ◽  
Ulf Bredolt
Keyword(s):  
Steady State ◽  
Sample Size ◽  
Void Fraction ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Statistical Uncertainty ◽  
Sampling Strategies ◽  
Latin Hypercube ◽  
Fine Mesh ◽  
Uncertainty Analyses

In recent years, a more realistic safety analysis of nuclear reactors has been based on best estimate (BE) computer codes. Because their predictions are unavoidably affected by conceptual, aleatory and experimental sources of uncertainty, an uncertainty analysis is needed if useful conclusions are to be obtained from BE codes. In this paper, statistical uncertainty analyses of cross-sectional averaged void fraction calculations using the POLCA-T system code, and based on the BWR Full-Size Fine-Mesh Bundle Test (BFBT) benchmark are presented by means of two different sampling strategies: Latin Hypercube (LHS) and Simple Random Sampling (SRS). LHS has the property of densely stratifying across the range of each input probability distribution, allowing a much better coverage of the input uncertainties than SRS. The aim here is to compare both uncertainty analyses on the BWR assembly void axial profile prediction in steady-state, and on the transient void fraction prediction at a certain axial level coming from a simulated re-circulation pump trip scenario. It is shown that the replicated void fraction mean (either in steady-state or transient conditions) has less variability when using LHS than SRS for the same number of calculations (i.e. same input space sample size) even if the resulting void fraction axial profiles are non-monotonic. It is also shown that the void fraction uncertainty limits achieved with SRS by running 458 calculations (sample size required to cover 95% of 8 uncertain input parameters with a 95% confidence), result in the same uncertainty limits achieved by LHS with only 100 calculations. These are thus clear indications on the advantages of using LHS.

Difference in Response Effort Across Sample Types: Perception or Reality?

2015 ◽  
Vol 8 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Shan Ran ◽  
Mengqiao Liu ◽  
Lisa A. Marchiondo ◽  
Jason L. Huang
Keyword(s):  
Internal Validity ◽  
Sampling Strategy ◽  
Careful Consideration ◽  
Sampling Strategies ◽  
Response Effort ◽  
Potential Influence ◽  
Organizational Research ◽  
Research And Practice ◽  
Research Findings ◽  
Research Questions

Landers and Behrend (2015) question organizational researchers’ stubborn reliance on sample source to infer the validity of research findings, and they challenge the arbitrary distinctions researchers often make between sample sources. Unconditional favoritism toward particular sampling strategies (e.g., organizational samples) can restrict choices in methodology, which in turn may limit opportunities to answer certain research questions. Landers and Behrend (2015) contend that no sampling strategy is inherently superior (or inferior), and therefore, all types of samples warrant careful consideration before any validity-related conclusions can be made. Despite sound arguments, the focal article focuses its consideration on external validity and deemphasizes the potential influence of sample source on internal validity. Agreeing with the position that no samples are the “gold standard” in organizational research and practice, we focus on insufficient effort responding (IER; Huang, Curran, Keeney, Poposki, & DeShon, 2012) as a threat to internal validity across sample sources.

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