LEED-NC 2009 SILVER TO GOLD CERTIFIED PROJECTS IN THE US IN 2012–2017: AN APPROPRIATE STATISTICAL ANALYSIS

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Abstract

This study aims to evaluate the Silver-to-Gold LEED-NC 2009 (Leadership in Energy and Environmental Design for New Construction and Major Renovations) cross-certification performance and categorize the cross-certification performance in eight US states in 2012–2017. The following three statistical analyses were used: (a) pooling LEED projects within a single state and single year in a single-state-year group with the subsequent use of a replication method, (b) pooling the medians of the LEED projects in each state from all years in a state-and-total-years group, and (c) pooling the LEED projects from all states and years in a total states-and-years group. The Silver-to-Gold cross-certification performance has a low propelling effect. Considering the Silver-to-Gold category cross-certification performances, the Energy and Atmosphere (EA) category has a high propelling effect, the Sustainable Sites (SS) and Environmental Quality (EQ) categories have moderate propelling effects, the Water Efficiency (WE), Materials and Resources (MR), and Innovation in Design (ID) categories have low propelling effects. Six of the eight states used an EA-high emphasized strategy, and two of the eight states used a SS/EA/WE/EQ/ID-moderate emphasized strategy. The single-state-year group and state-and-total-years group analyses are more robust than the pooling LEED projects using the total state-and-year group analysis.

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A power primer.

Jacob Cohen (1992)

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

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Effect size, confidence interval and statistical significance: a practical guide for biologists.

Shinichi Nakagawa, Innes Cuthill (2007)

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.