Signal-to-Noise Ratio on Test Science Research Document Library https://research.testscience.org/keywords/signal-to-noise-ratio/ Recent content in Signal-to-Noise Ratio on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Mon, 01 Jan 2024 00:00:00 +0000 Simulation Insights on Power Analysis with Binary Responses--from SNR Methods to 'skprJMP' https://research.testscience.org/post/2024-simulation-insights-on-power-analysis-with-binary-responses-from-snr-methods-to-skprjmp/ Mon, 01 Jan 2024 00:00:00 +0000 https://research.testscience.org/post/2024-simulation-insights-on-power-analysis-with-binary-responses-from-snr-methods-to-skprjmp/ Logistic regression is a commonly-used method for analyzing tests with probabilistic responses in the test community, yet calculating power for these tests has historically been challenging. This difficulty prompted the development of methods based on signal-to-noise ratio (SNR) approximations over the last decade, tailored to address the intricacies of logistic regression’s binary outcomes. However, advancements and improvements in statistical software and computational power have reduced the need for such approximate methods.

Logistic regression is a commonly-used method for analyzing tests with probabilistic responses in the test community, yet calculating power for these tests has historically been challenging. This difficulty prompted the development of methods based on signal-to-noise ratio (SNR) approximations over the last decade, tailored to address the intricacies of logistic regression’s binary outcomes. However, advancements and improvements in statistical software and computational power have reduced the need for such approximate methods. Our research presents a detailed simulation study that compares SNR-based power estimates with those derived from exact Monte Carlo simulations, highlighting the inadequacies of SNR approximations. To address these shortcomings, we will discuss improvements in the open-source R package “skpr” as well as present “skprJMP,” a new plug-in that offers more accurate and reliable power calculations for logistic regression analyses.

Suggested Citation

Atkins, Robert, Tyler Morgan-Wall, and Curtis Miller. “With Binary Responses–From SNR Methods to ‘skprJMP.’” Institute for Defense Analyses IDA Product ID 3002093 (April 2024).

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]]> Power Approximations for Generalized Linear Models using the Signal-to-Noise Transformation Method https://research.testscience.org/post/2017-power-approximations-for-generalized-linear-models-using-the-signal-to-noise-transformation-method/ Sun, 01 Jan 2017 00:00:00 +0000 https://research.testscience.org/post/2017-power-approximations-for-generalized-linear-models-using-the-signal-to-noise-transformation-method/ Statistical power is a useful measure for assessing the adequacy of anexperimental design prior to data collection. This paper proposes an approach referredto as the signal-to-noise transformation method (SNRx), to approximate power foreffects in a generalized linear model. The contribution of SNRx is that, with a coupleassumptions, it generates power approximations for generalized linear model effectsusing F-tests that are typically used in ANOVA for classical linear models.Additionally, SNRx follows Ohlert and Whitcomb’s unified approach for sizing aneffect, which allows for intuitive effect size definitions, and consistent estimates ofpower. Statistical power is a useful measure for assessing the adequacy of anexperimental design prior to data collection. This paper proposes an approach referredto as the signal-to-noise transformation method (SNRx), to approximate power foreffects in a generalized linear model. The contribution of SNRx is that, with a coupleassumptions, it generates power approximations for generalized linear model effectsusing F-tests that are typically used in ANOVA for classical linear models.Additionally, SNRx follows Ohlert and Whitcomb’s unified approach for sizing aneffect, which allows for intuitive effect size definitions, and consistent estimates ofpower. This paper details the process for defining an effect size, constructing thecoefficients for the test, and calculating power for the family of generalized linearmodels. The focus is on experimental designs that have multi-level categorical factors. A simulation study is performed which demonstrates that SNRx power results agreewith simulation.

Suggested Citation

Johnson, Thomas H., Laura Freeman, Jim Simpson, and Colin Anderson. “Power Approximations for Generalized Linear Models Using the Signal-to-Noise Transformation Method.” Quality Engineering 30, no. 3 (July 3, 2018): 511–24. https://doi.org/10.1080/08982112.2017.1361537.

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