Related Experiment Video
Updated: Feb 20, 2026

08:24
The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
Published on: August 25, 2023
1.2K
Decision theory for comparing institutions
1Department of Medicine, Imperial College, London, UK.
Statistics in Medicine
|October 17, 2017
Summary
This study introduces a decision-theory approach to performance assessment for public services, integrating diverse user perspectives for clearer results and actionable choices.
Area of Science:
- Public Administration
- Decision Theory
- Performance Measurement
Background:
- Performance assessments are crucial for public service institutions like hospitals and schools.
- Interpreting assessment results is challenging due to inherent uncertainties and varied stakeholder perspectives.
- Existing formats often fail to adequately address the needs of diverse users, including managers, consumers, and regulatory bodies.
Purpose of the Study:
- To develop a novel decision-theoretical framework for performance assessment in public service institutions.
- To integrate multiple stakeholder perspectives into a unified analytical approach.
- To generate clear, actionable choices from assessment results.
Main Methods:
- A decision-theoretical approach was employed to analyze performance assessment data.
- The methodology focuses on integrating diverse user perspectives within the analysis.
- Results are framed as a selection from a predefined list of alternative actions.
Main Results:
- The proposed approach facilitates a more comprehensive interpretation of performance assessment outcomes.
- It effectively integrates the viewpoints of various stakeholders, including managers and central authorities.
- The framework yields practical choices for action, addressing the limitations of traditional assessment formats.
Conclusions:
- A decision-theoretical approach offers a robust solution for the complexities of public service performance assessment.
- Integrating diverse perspectives enhances the utility and interpretability of assessment results.
- This method provides a clear pathway to informed decision-making for public institutions.
Related Concept Videos
Decision Making: P-value Method
7.0K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.0K
Decision Making: Traditional Method
5.6K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.6K
Multiple Comparison Tests
4.5K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.5K
Comparing Experimental Results: Student's t-Test
6.1K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
6.1K
Friedman Two-way Analysis of Variance by Ranks
518
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
518
Comparing the Survival Analysis of Two or More Groups
637
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
637

