Chapter 7 | Statistical Procedures

2020 ◽  
pp. 115-154
Author(s):  
John C. Castura ◽  
Brian C. Franczak
Keyword(s):  
Statistical Procedures

Which Data to Meta-Analyze, and How?

2019 ◽  
Vol 227 (1) ◽  
pp. 64-82 ◽  
Author(s):  
Martin Voracek ◽  
Michael Kossmeier ◽  
Ulrich S. Tran
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Curve Analysis ◽  
Research Progress ◽  
Primary Data ◽  
Statistical Procedures ◽  
Graphical Displays ◽  
Primary Data Analysis ◽  
Meta Analyses ◽  
Data Analytic

Abstract. Which data to analyze, and how, are fundamental questions of all empirical research. As there are always numerous flexibilities in data-analytic decisions (a “garden of forking paths”), this poses perennial problems to all empirical research. Specification-curve analysis and multiverse analysis have recently been proposed as solutions to these issues. Building on the structural analogies between primary data analysis and meta-analysis, we transform and adapt these approaches to the meta-analytic level, in tandem with combinatorial meta-analysis. We explain the rationale of this idea, suggest descriptive and inferential statistical procedures, as well as graphical displays, provide code for meta-analytic practitioners to generate and use these, and present a fully worked real example from digit ratio (2D:4D) research, totaling 1,592 meta-analytic specifications. Specification-curve and multiverse meta-analysis holds promise to resolve conflicting meta-analyses, contested evidence, controversial empirical literatures, and polarized research, and to mitigate the associated detrimental effects of these phenomena on research progress.

The Impact of Terrorism on Construction Industry in Iraq

2015 ◽  
Vol 3 (2) ◽  
pp. 69-84
Author(s):  
Wadhah Amer Hatem ◽  
Samiaah M. Hassen Al-Tmeemy
Keyword(s):  
Construction Industry ◽  
Construction Projects ◽  
Historical Data ◽  
Terrorist Attacks ◽  
Construction Sector ◽  
Statistical Procedures ◽  
Qualitative Approaches ◽  
Suicide Attacks ◽  
The Face ◽  
The Impact

     Suicide attacks, bombings, explosions became the part of daily life in Iraq. Consequently, the threat of terrorism put the Iraqi construction sector in the face of unique and unusual challenges that not seen on other countries. These challenges can have extensive impact on construction projects. This paper seeks to examine the impact of the terrorist attacks on construction industry and determine the extent to which the impact of terrorism on construction projects in terms of cost, schedule, and quality. This study adapted quantitative and qualitative approaches to collect data using questionnaire survey and interviews, as well as historical data. The study focused on projects that have been the target of terrorist strikes in Diyala governorate. A variety of statistical procedures were employed in data analysis. The results revealed the extent to which terrorist attacks impact construction projects in terms of cost, time, and quality. The results of this study will enhance the awareness of all construction parties to the impact of the terrorist attacks against construction projects. Eventually, this can develop a risk management assessment and assist contractors to properly protect projects and buildings to minimize injuries and fatalities in the event of terrorism.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

A robust random coefficient regression representation of the chain-ladder method

2021 ◽  
pp. 1-32
Author(s):  
Ioannis Badounas ◽  
Apostolos Bozikas ◽  
Georgios Pitselis
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Random Coefficients ◽  
Regression Coefficients ◽  
Varying Coefficients ◽  
Statistical Procedures ◽  
Chain Ladder Method ◽  
Loss Reserving ◽  
Random Coefficient Regression ◽  
Chain Ladder

Abstract It is well known that the presence of outliers can mis-estimate (underestimate or overestimate) the overall reserve in the chain-ladder method, when we consider a linear regression model, based on the assumption that the coefficients are fixed and identical from one observation to another. By relaxing the usual regression assumptions and applying a regression with randomly varying coefficients, we have a similar phenomenon, i.e., mis-estimation of the overall reserves. The lack of robustness of loss reserving regression with random coefficients on incremental payment estimators leads to the development of this paper, aiming to apply robust statistical procedures to the loss reserving estimation when regression coefficients are random. Numerical results of the proposed method are illustrated and compared with the results that were obtained by linear regression with fixed coefficients.

scholarly journals A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
David Trafimow
Keyword(s):  
Present Article ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
A Priori ◽  
Significance Testing ◽  
Population Parameters ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Statistical Procedures ◽  
Major Section

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

Landsat Views of Sudbury (Canada) Area Acidic and Nonacidic Lakes

1992 ◽  
Vol 49 (S1) ◽  
pp. 33-39 ◽  
Author(s):  
J. Roger Pitblado
Keyword(s):  
Thematic Mapper ◽  
Landsat Data ◽  
Multivariate Statistical ◽  
Temporal Dimension ◽  
Acidic Lake ◽  
Water Colour ◽  
Statistical Procedures ◽  
Changes Over Time ◽  
Radiance Data ◽  
Over Time

Multivariate statistical procedures are used to establish empirical associations between acidity, visual lake water colour, dissolved organic carbon (DOC) concentrations, and Landsat-5 Thematic Mapper (TM) radiance data. Acidic lakes in an area northeast of Sudbury (Canada) are characterized by their clear, blue colours and very low DOC. With a subjective, three-class water colour grouping, 92% of the study lakes were correctly classified using TM data. Further, it is shown that DOC, the major component of water colour in this area, can be predicted within 1 mg/L of observed concentrations using TM data (multiple r = 0.93, P < 0.01). By deriving interrelationships between pH levels, water colour, and DOC, Landsat data provide a means to discriminate and map the acidic and nonacidic lakes of the study area. Examination of the reflectance characteristics of a single acidic lake (Bowland Lake) that has undergone neutralization suggests that Landsat data may be used to detect optical changes over time. However, the capability for monitoring the temporal dimension of lake acidification using satellite data has yet to be established.

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