Results 1  10
of
149
Mediation in experimental and nonexperimental studies: new procedures and recommendations
 PSYCHOLOGICAL METHODS
, 2002
"... Mediation is said to occur when a causal effect of some variable X on an outcome Y is explained by some intervening variable M. The authors recommend that with small to moderate samples, bootstrap methods (B. Efron & R. Tibshirani, 1993) be used to assess mediation. Bootstrap tests are powerful ..."
Abstract

Cited by 696 (4 self)
 Add to MetaCart
(Show Context)
Mediation is said to occur when a causal effect of some variable X on an outcome Y is explained by some intervening variable M. The authors recommend that with small to moderate samples, bootstrap methods (B. Efron & R. Tibshirani, 1993) be used to assess mediation. Bootstrap tests are powerful because they detect that the sampling distribution of the mediated effect is skewed away from 0. They argue that R. M. Baron and D. A. Kenny’s (1986) recommendation of first testing the X → Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility. Empirical examples and computer setups for bootstrap analyses are provided. Mediation models of psychological processes are popular because they allow interesting associations to be decomposed into components that reveal possible causal mechanisms. These models are useful for theory development and testing as well as for the identification of possible points of intervention in applied work. Mediation is equally of interest to experimental psychologists as it is to those who study naturally occurring processes through nonexperimental studies. For example, social–cognitive psychologists are interested in showing that the effects of cognitive priming on attitude change are mediated by the accessibility of certain beliefs (Eagly & Chaiken, 1993). Developmental psychologists use longitudinal methods to study how parental unemployment can have adverse effects on child behavior through its intervening effect on quality of parenting (Conger et al., 1990). Mediation analysis is also used in organizational
Statistical significance testing and cumulative knowledge in psychology: Implications for the training of researchers
 Psychological Methods
, 1996
"... Data analysis methods in psychology still emphasize statistical significance testing, despite numerous articles demonstrating its severe deficiencies. It is now possible to use metaanalysis to show that reliance on significance testing retards the development of cumulative knowledge. But reform of ..."
Abstract

Cited by 206 (0 self)
 Add to MetaCart
(Show Context)
Data analysis methods in psychology still emphasize statistical significance testing, despite numerous articles demonstrating its severe deficiencies. It is now possible to use metaanalysis to show that reliance on significance testing retards the development of cumulative knowledge. But reform of teaching and practice will also require that researchers learn that the benefits that they believe flow from use of significance testing are illusory. Teachers must revamp their courses to bring students to understand that (a) reliance on significance testing retards the growth of cumulative research knowledge; (b) benefits widely believed to flow from significance testing do not in fact exist; and (c) significance testing methods must be replaced with point estimates and confidence intervals in individual studies and with metaanalyses in the integration of multiple studies. This reform is essential to the future progress of cumulative knowledge in psychological research. In 1990, Aiken, West, Sechrest, and Reno published an important article surveying the teaching of quantitative methods in graduate psychology programs. They were concerned about what was not being taught or was being inadequately taught to future researchers and the harm this might cause to research progress in psychology. For example, they found that new and important quantitative methods such as causal modeling, confirmatory factor analysis, and metaanalysis were not being taught in the majority of graduate programs. This is indeed a legitimate cause for concern. But in this article, I am concerned about the opposite: An earlier version of this article was presented as the presidential address to the Division of Evaluation,
AERA editorial policies regarding statistical significance testing: Three suggested reforms
 Educational Researcher
, 1996
"... comments on Thompson (1996), it is argued that describing results as "significant " rather than "statistically significant " is confusing to those persons most susceptible to misinterpreting this telegraphic wording. Contrary to Robinson and Levin's view, it is noted tha ..."
Abstract

Cited by 118 (7 self)
 Add to MetaCart
(Show Context)
comments on Thompson (1996), it is argued that describing results as "significant " rather than "statistically significant " is confusing to those persons most susceptible to misinterpreting this telegraphic wording. Contrary to Robinson and Levin's view, it is noted that the utility of the characterization of results as being due to "nonchance " is limited by the nature of the null hypothesis assumed to be true. It is suggested that effect sizes are important to interpret, even though they too can be misinterpreted; recent empirical studies of publications indicate that effect sizes are still too rarely reported. Finally, the value of "external " replicability analyses is acknowledged, but it is argued that "internal " replicability analyses can also be useful, and certainly are superior to statistical significance tests regarding evaluating result replicability, because statistical significance tests do not evaluate replicability.
Null Hypothesis Significance Testing: A Review of an Old and Continuing Controversy
 Psychological Methods
, 2000
"... Null hypothesis significance testing (NHST) is arguably the mosl widely used approach to hypothesis evaluation among behavioral and social scientists. It is also very controversial. A major concern expressed by critics is that such testing is misunderstood by many of those who use it. Several other ..."
Abstract

Cited by 97 (0 self)
 Add to MetaCart
(Show Context)
Null hypothesis significance testing (NHST) is arguably the mosl widely used approach to hypothesis evaluation among behavioral and social scientists. It is also very controversial. A major concern expressed by critics is that such testing is misunderstood by many of those who use it. Several other objections to its use have also been raised. In this article the author reviews and comments on the claimed misunderstandings as well as on other criticisms of the approach, and he notes arguments that have been advanced in support of NHST. Alternatives and supplements to NHST are considered, as are several related recommendations regarding the interpretation of experimental data. The concluding opinion is that NHST is easily misunderstood and misused but that when applied with good judgment it can be an effective aid to the interpretation of experimental data. Null hypothesis statistical testing (NHST1) is arguably the most widely used method of analysis of data collected in psychological experiments and has been so for about 70 years. One might think that a method that had been embraced by an entire research community would be well understood and noncontroversial after many decades of constant use. However, NHST is very controversial.2 Criticism of the method, which essentially began with the introduction of the technique (Pearce, 1992), has waxed and waned over the years; it has been intense in the recent past. Apparently, controversy regarding the idea of NHST more generally extends back more than two and a half
The insignificance of statistical significance testing.
 Journal of Wildlife Management,
, 1999
"... Abstract: Despite their wide use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are o ..."
Abstract

Cited by 92 (0 self)
 Add to MetaCart
(Show Context)
Abstract: Despite their wide use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of Pvalues, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.
Measures of effect size for comparative studies: Applications, interpretations, and limitations
 Contemporary Educational Psychology
, 2000
"... Although dissatisfaction with the limitations associated with tests for statistical significance has been growing for several decades, applied researchers have continued to rely almost exclusively on these indicators of effect when reporting their findings. To encourage an increased use of alternat ..."
Abstract

Cited by 80 (0 self)
 Add to MetaCart
(Show Context)
Although dissatisfaction with the limitations associated with tests for statistical significance has been growing for several decades, applied researchers have continued to rely almost exclusively on these indicators of effect when reporting their findings. To encourage an increased use of alternative measures of effect, the present paper discusses several measures of effect size that might be used in group comparison studies involving univariate and/or multivariate models. For the methods discussed, formulas are presented and data from an experimental study are used to demonstrate the application and interpretation of these indices. The paper concludes with some cautionary notes on the limitations associated with these measures of effect size. ª 2000 Academic Press For more than three decades data analysts have been recommending to researchers in the behavioral sciences that, in addition to a test for statistical significance, an effect size measure should also be reported with their findings (Cohen, 1965; Hays, 1963). The rationale for this recommendation rests on the fact that statistical significance does not imply meaningfulness. ‘‘Significance’ ’ based on a statistical test provides information on the likelihood of finding the observed relationship by chance alone (sampling error). While statistical ‘‘significance’ ’ helps to protect the researcher from interpreting an apparently large observed difference as meaning a true difference between populations when sample sizes are small, it does not protect the researcher from interpreting a trivially small observed difference as meaningful when sample sizes are large. Small differences can be statistically ‘‘significant’’
Psychology will be a much better science when we change the way we analyze data
 Current Directions in Psychological Science
, 1996
"... because I believed that within it dwelt some of the most fundamental and challenging problems of the extant sciences. Who could not be intrigued, for example, by the relation between consciousness and behavior, or the rules guiding interactions in social situations, or the processes that underlie de ..."
Abstract

Cited by 78 (3 self)
 Add to MetaCart
because I believed that within it dwelt some of the most fundamental and challenging problems of the extant sciences. Who could not be intrigued, for example, by the relation between consciousness and behavior, or the rules guiding interactions in social situations, or the processes that underlie development from infancy to maturity? Today, in 1996, my fascination with these problems is undiminished. But I've developed a certain angst over the intervening thirtysomething years—a constant, nagging feeling that our field spends a lot of time spinning its wheels without really making all that much progress. This problem shows up in obvious ways—for instance, in the regularity with which findings seem not to replicate. It also shows up in subtler ways—for instance, one doesn't often hear Psychologists saying, "Well this problem is solved now; let's move on to the next one " (as, for example, Johannes Kepler must have said over three centuries ago, after he had cracked the problem of describing planetary motion). I've come to believe that at least part of this problem revolves around our tools—particularly the tools that we use in the critical domains of data analysis and data interpretation. What we do, I sometimes feel, is akin to trying to build a violin using a stone mallet and a chainsaw. The tooltotask fit is not all that good, and as a result, we wind up building a lot of poorquality violins. My purpose here is to elaborate on these issues. In what follows, I will summarize our major dataanalysis and datainterpretation tools, and describe what I believe to be amiss with them. I will then offer some suggestions for change.
Title: Why do we still use stepwise modelling in ecology and behaviour?
"... 1. The biases and shortcomings of stepwise multiple regression are well established within the statistical literature. However an examination of papers published in 2004 by three leading ecological and behavioural journals suggested that the use of this technique remains widespread: of 65 papers in ..."
Abstract

Cited by 71 (1 self)
 Add to MetaCart
1. The biases and shortcomings of stepwise multiple regression are well established within the statistical literature. However an examination of papers published in 2004 by three leading ecological and behavioural journals suggested that the use of this technique remains widespread: of 65 papers in which a multiple regression approach was used, 57 % of studies used a stepwise procedure. 2. The principal drawbacks of stepwise multiple regression include bias in parameter estimation, inconsistencies among model selection algorithms, an inherent (but often overlooked) problem of multiple hypothesis testing, and an inappropriate focus or reliance on a single best model. We discuss each of these issue with examples. 3. We use a worked example of data on yellowhammer distribution collected over four years to highlight the pitfalls of stepwise regression. We show that stepwise regression allows models containing significant predictors to be obtained from each year’s data. In spite of the significance of the selected models, they vary substantially between years and suggest patterns that are at odds with those determined by analysing the full, four year data set. 4. An Information Theoretic (IT) analysis of the yellowhammer data set illustrates why the varying outcomes of stepwise analyses arise. In particular, the IT approach identifies large numbers of competing models that could describe the data equally well, showing that no one model should be relied upon for inference. 2
How to estimate and interpret various effect sizes
 Journal of Counseling Psychology
, 2004
"... The present article presents a tutorial on how to estimate and interpret various effect sizes. The 5th edition ..."
Abstract

Cited by 44 (0 self)
 Add to MetaCart
The present article presents a tutorial on how to estimate and interpret various effect sizes. The 5th edition
On the logic and purpose of significance testing.
 Psychological Methods,
, 1997
"... There has been much recent attention given to the problems involved with the traditional approach to null hypothesis significance testing (NHST). Many have suggested that, perhaps, NHST should be abandoned altogether in favor of other bases for conclusions such as confidence intervals and effect si ..."
Abstract

Cited by 43 (1 self)
 Add to MetaCart
(Show Context)
There has been much recent attention given to the problems involved with the traditional approach to null hypothesis significance testing (NHST). Many have suggested that, perhaps, NHST should be abandoned altogether in favor of other bases for conclusions such as confidence intervals and effect size estimates (e.g., The topic of this article is null hypothesis significance testing (NHST;