Related Experiment Video
Updated: Jun 4, 2025
09:09
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
Published on: September 27, 2024
400
From missing data to informative GPA predictions: Navigating selection process beliefs with the partial
Eduardo Alarcón-Bustamante1,2,3,4, Jorge González3,4,5, David Torres Irribarra1,3,4
1Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
The British Journal of Mathematical and Statistical Psychology
|December 24, 2024
Summary
Predicting college GPA from admissions test scores is challenging due to missing data for non-selected applicants. This study uses partial identifiability theory with milder assumptions to improve regression analysis for admissions data.
Area of Science:
- Educational Measurement
- Statistics
- Higher Education
Background:
- Predictive validity studies commonly use regression analysis to assess college admissions test scores' ability to forecast college GPA.
- A key challenge is the missing data problem: test scores are available for all applicants, but GPAs are only observed for admitted students.
Purpose of the Study:
- To present an alternative approach to handling missing data in predictive validity studies of college admissions.
- To explore how results vary based on assumptions made about the selection process.
Main Methods:
- Utilized the theory of partial identifiability to address missing data in regression analyses.
- Applied milder assumptions compared to standard methods that require strong assumptions for data identification.
Main Results:
- Demonstrated that results from regression analyses can significantly differ based on the assumptions employed regarding the admissions selection process.
- Showcased the application of the partial identifiability approach using a university admissions dataset.
Conclusions:
- The theory of partial identifiability offers a flexible framework for analyzing predictive validity in college admissions under various assumption sets.
- Emphasizes the importance of carefully considering and stating assumptions when evaluating the predictive power of admissions tests.
Related Concept Videos
02:13The Representativeness Heuristic
15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K
01:12Hindsight Biases
3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
3.4K
01:09Decision Making: P-value Method
5.3K
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...
5.3K
01:02Quantifying and Rejecting Outliers: The Grubbs Test
1.5K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
01:29Reliability and Validity
12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.7K
01:14Fundamental Attribution Error
12.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
12.8K