Statistical Power and Effect Size in Marketing Research

Sawyer, Alan G.; Ball, A. Dwayne

doi:10.2307/3150969

Cited by 117 publications

(43 citation statements)

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“…A stepwise procedure was chosen so as to identify independent variables minimising the effects of multicollinearity. Table 7 shows that, in accordance with Cohen ' s table, 97 the correlations between the dimensions of the relationship approach and the dimensions of the use of Internet banking all have, between them, a weak power of explanation ( R 2 < 0,13). Even so, the strongest correlation is between the duration / continuity of the relationship between the customer and his bank and the diversity of places of access, concluding that the greater the diversity of places from which the customer accesses Internet banking, the greater the probability that the customer has had a relationship with his bank for several The results show that the relationship marketing approach is related to the intensity of use of Internet banking as well as to the diversity of operations performed there.…”

Section: Methodology and Resultsmentioning

confidence: 69%

The impact of the Internet upon bank marketing

2010

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Section: Methodology and Resultsmentioning

confidence: 69%

The impact of the Internet upon bank marketing

2010

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“…Sufficient data were collected to calculate observed study power [32][33][34] for the endpoint of mortality at final follow-up.…”

Section: Data Extractionmentioning

confidence: 99%

Randomised trials of 6 % tetrastarch (hydroxyethyl starch 130/0.4 or 0.42) for severe sepsis reporting mortality: systematic review and meta-analysis

2013

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PurposeTo assess the impact of 6% tetrastarch (hydroxyethyl starch (HES) 130/0.4 and 130/0.42) in severe sepsis patients. The primary outcome measure was 90-day mortality. MethodsA structured literature search to identify prospective randomized controlled trials (RCT) in adult patients with severe sepsis receiving 6% tetrastarch (potato or waxy maize origin), as part of fluid resuscitation in comparison to other non-HES fluids after randomisation, in the critical care setting was undertaken. A systematic review and meta-analysis was performed. ResultsSix RCTs were included (n = 3033): three from 2012 (n = 2913) had a low risk of bias. Median tetrastarch exposure was 37.4 ml/kg (range 30 to 43). Ninety-day mortality was associated with tetrastarch exposure (relative risk (RR) 1.13; 95% confidence interval (CI) 1.02 to 1.25; p = 0.02) compared to crystalloid. The number needed to harm (NNH) was 28.8 (95% CI 14.6 to 942.5). Publication bias and statistical heterogeneity (I 2 = 0%) were not present. Tetrastarch exposure was also associated with renal replacement therapy (p = 0.01; NNH 15.7) and allogeneic transfusion support (p = 0.001; NNH 9.9). No difference between groups was observed for 28-day mortality, comparison with colloid as control, and for waxy maize derived tetrastarch, but power was lacking. Overall mortality was associated with tetrastarch exposure (RR 1.13; 95% CI 1.02 to 1.25; p = 0.02). ConclusionsIn our analysis, 6% tetrastarch as part of initial fluid resuscitation for severe sepsis was associated with harm, and as alternatives exist, in our view should be avoided.

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“…In 1962, Cohen showed in a famous article that most research in abnormal social psychology was severely underpowered and actually led to the failure to reject null hypotheses which were actually false (Cohen, 1962). Following Cohen, many studies on statistical power have been conducted in psychological research (Brewer, 1972;Katzer and Sodt, 1973;Haase, 1974;Chase and Tucker, 1975;Kroll and Chase, 1975;Chase and Baran, 1976;Sawyer and Ball, 1981;Sedlmeier and Gigerenzer, 1989). The result of these surveys is alarming, because they point out that a large part of experimental research reveal effects that are actually false, due to statistical underpower.…”

Section: The Birth Of a Long-standing Statistical Controversy In Psycmentioning

confidence: 99%

Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics

Vallois

Jullien

2018

SSRN Journal

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Abstract.Experimental economists increasingly apply econometric techniques to interpret their data, as suggests the emergence of "experimetrics" in the 2000s. Yet statistics remains a minor topic in historical and methodological writings on experimental economics (EE). This article aims to address this lacuna .To do so, we analyze the use of statistical tools in EE from early economics experiments of the 1940s-1950s to the present days. Our narrative is based on qualitative analysis of papers published in early periods and quantitative analysis of papers published in more recent periods. Our results reveal a significant change in EE' statistical methods, namely an evolution from purely descriptive methods to more sophisticated and standardized techniques. We also highlight that, despite the decisive role played by statistics in the way EE estimate the rationality of individuals or markets, statistics are still considered as involving non-methodological issues, i.e., as involving only purely technical issues. Our historical analysis shows that this technical conception was the result of a longrun evolution of the process of scientific legitimization of EE, which allowed experimental economists to escape from psychologist's more reflexive culture toward statistics.

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Statistical Power and Effect Size in Marketing Research

Cited by 117 publications

References 0 publications

The impact of the Internet upon bank marketing

The impact of the Internet upon bank marketing

Randomised trials of 6 % tetrastarch (hydroxyethyl starch 130/0.4 or 0.42) for severe sepsis reporting mortality: systematic review and meta-analysis

Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics

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