Logistic Regression on Test Science Research Document Library https://research.testscience.org/keywords/logistic-regression/ Recent content in Logistic Regression 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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]]> Analysis Apps for the Operational Tester https://research.testscience.org/post/2022-analysis-apps-for-the-operational-tester/ Sat, 01 Jan 2022 00:00:00 +0000 https://research.testscience.org/post/2022-analysis-apps-for-the-operational-tester/ In the acquisition and testing world, data analysts repeatedly encounter certain categories of data, such as time or distance until an event (e.g., failure, alert, detection), binary outcomes (e.g., success/failure, hit/miss), and survey responses. Analysts need tools that enable them to produce quality and timely analyses of the data they acquire during testing. This poster presents four web-based apps that can analyze these types of data. The apps are designed to assist analysts and researchers with simple repeatable analysis tasks, such as building summary tables and plots for reports or briefings. In the acquisition and testing world, data analysts repeatedly encounter certain categories of data, such as time or distance until an event (e.g., failure, alert, detection), binary outcomes (e.g., success/failure, hit/miss), and survey responses. Analysts need tools that enable them to produce quality and timely analyses of the data they acquire during testing. This poster presents four web-based apps that can analyze these types of data. The apps are designed to assist analysts and researchers with simple repeatable analysis tasks, such as building summary tables and plots for reports or briefings. Using software tools like these apps can increase reproducibility of results, timeliness of analysis and reporting, attractiveness and standardization of aesthetics in figures, and accuracy of results. The first app models reliability of a system or component by fitting parametric statistical distributions to time-to-failure data. The second app fits a logistic regression model to binary data with one or two independent continuous variables as The third calculates summary statistics and produces plots of groups of Likert-scale survey question responses. The fourth calculates the system usability scale (SUS) scores for SUS survey responses and enables the app user to plot scores versus an independent variable. These apps are available for public use on the Test Science Interactive Tools webpage https://testscience.org/interactive-tools/.

Suggested Citation

Lillard, V Bram, and William Whitledge. Analysis Apps for the Operational Tester. IDA Document NS D-32959. Alexandria, VA: Institute for Defense Analyses, 2022.

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]]> D-Optimal as an Alternative to Full Factorial Designs- a Case Study https://research.testscience.org/post/2019-d-optimal-as-an-alternative-to-full-factorial-designs-a-case-study/ Tue, 01 Jan 2019 00:00:00 +0000 https://research.testscience.org/post/2019-d-optimal-as-an-alternative-to-full-factorial-designs-a-case-study/ The use of Bayesian statistics and experimental design as tools to scope testing and analyze data related to defense has increased in recent years. Planning a test using experimental design will allow testers to cover the operational space while maximizing the information obtained from each run. Understanding which factors can affect a detector’s performance can influence military tactics, techniques and procedures, and improve a commander’s situational awareness when making decisions in an operational environment. The use of Bayesian statistics and experimental design as tools to scope testing and analyze data related to defense has increased in recent years. Planning a test using experimental design will allow testers to cover the operational space while maximizing the information obtained from each run. Understanding which factors can affect a detector’s performance can influence military tactics, techniques and procedures, and improve a commander’s situational awareness when making decisions in an operational environment. This presentation will explain how a D-optimal experimental design could be an option for planning a test when the number of runs is limited but an adequate test is desired. Additionally, it will describe how the results of a Bayesian multiple logistic model could be used to show in what way the operational environment can affect the detector’s performance.

Suggested Citation

Anderson, Breeana G, Heather M Wojton, and Keyla Pagan-Rivera. D-Optimal as an Alternative to Full Factorial Designs: A Case Study. IDA Document NS D-10580. Alexandria, VA: Institute for Defense Analyses, 2019.

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