scholarly journals Z Scores, Standard Scores, and Composite Test Scores Explained

2021 ◽  
pp. 025371762110465
Author(s):  
Chittaranjan Andrade
Keyword(s):  
Statistical Methods ◽  
Test Scores ◽  
Test Score ◽  
Composite Score ◽  
Simple Addition ◽  
Z Scores ◽  
The Individual

Patients may be assessed using a battery of tests where different tests yield scores in different units, where different tests have different minimum and maximum scores, and where higher or lower scores mean different things in different tests. Therefore, a composite test score cannot be obtained by simple addition or averaging of scores in the individual tests. However, if performances in individual tests are converted to Z scores, the Z scores can be added or averaged to yield a composite score that can be interpreted or processed using conventional statistical methods. This article explains in simple ways how Z scores are calculated, what the properties of Z scores are, how Z scores can be interpreted, and how Z scores can be converted into other standard scores.

Implications of Person Fluctuation for the Stability and Validity of Test Scores

Methodology ◽  
2006 ◽  
Vol 2 (4) ◽  
pp. 142-148 ◽  
Author(s):  
Pere J. Ferrando
Keyword(s):  
Item Response ◽  
Test Scores ◽  
Temporal Stability ◽  
Person Fit ◽  
Item Parameters ◽  
Individual Trait ◽  
The Stability ◽  
The Individual ◽  
Different Levels ◽  
Fluctuation Model

In the IRT person-fluctuation model, the individual trait levels fluctuate within a single test administration whereas the items have fixed locations. This article studies the relations between the person and item parameters of this model and two central properties of item and test scores: temporal stability and external validity. For temporal stability, formulas are derived for predicting and interpreting item response changes in a test-retest situation on the basis of the individual fluctuations. As for validity, formulas are derived for obtaining disattenuated estimates and for predicting changes in validity in groups with different levels of fluctuation. These latter formulas are related to previous research in the person-fit domain. The results obtained and the relations discussed are illustrated with an empirical example.

Missing Data in Research on Youth and Family Programs

2021 ◽  
pp. 003329412110268
Author(s):  
Jaime Ballard ◽  
Adeya Richmond ◽  
Suzanne van den Hoogenhof ◽  
Lynne Borden ◽  
Daniel Francis Perkins
Keyword(s):  
Missing Data ◽  
Statistical Methods ◽  
Self Report ◽  
Program Participation ◽  
Multilevel Data ◽  
Individual Level ◽  
Level Data ◽  
Before And After ◽  
Post Test ◽  
The Individual

Background Multilevel data can be missing at the individual level or at a nested level, such as family, classroom, or program site. Increased knowledge of higher-level missing data is necessary to develop evaluation design and statistical methods to address it. Methods Participants included 9,514 individuals participating in 47 youth and family programs nationwide who completed multiple self-report measures before and after program participation. Data were marked as missing or not missing at the item, scale, and wave levels for both individuals and program sites. Results Site-level missing data represented a substantial portion of missing data, ranging from 0–46% of missing data at pre-test and 35–71% of missing data at post-test. Youth were the most likely to be missing data, although site-level data did not differ by the age of participants served. In this dataset youth had the most surveys to complete, so their missing data could be due to survey fatigue. Conclusions Much of the missing data for individuals can be explained by the site not administering those questions or scales. These results suggest a need for statistical methods that account for site-level missing data, and for research design methods to reduce the prevalence of site-level missing data or reduce its impact. Researchers can generate buy-in with sites during the community collaboration stage, assessing problematic items for revision or removal and need for ongoing site support, particularly at post-test. We recommend that researchers conducting multilevel data report the amount and mechanism of missing data at each level.

Abiding by the Law? Using Benford’s Law to Examine the Accuracy of Nonprofit Financial Reports

2019 ◽  
Vol 49 (3) ◽  
pp. 548-570 ◽  
Author(s):  
Heng Qu ◽  
Richard Steinberg ◽  
Ronelle Burger
Keyword(s):  
Statistical Methods ◽  
Organizational Level ◽  
Benford’S Law ◽  
Financial Reports ◽  
Forensic Accounting ◽  
Data Form ◽  
Benford's Law ◽  
Natural Data ◽  
The Individual ◽  
Academic Study

Benford’s Law asserts that the leading digit 1 appears more frequently than 9 in natural data. It has been widely used in forensic accounting and auditing to detect potential fraud, but its application to nonprofit data is limited. As the first academic study that applies Benford’s Law to U.S. nonprofit data (Form 990), we assess its usefulness in prioritizing suspicious filings for further investigation. We find close conformity with Benford’s Law for the whole sample, but at the individual organizational level, 34% of the organizations do not conform. Deviations from Benford’s law are smaller for organizations that are more professional, that report positive fundraising and administration expenses, and that face stronger funder oversight. We suggest improved statistical methods and experiment with a new measure of the extent of deviation from Benford’s Law that has promise as a more discriminating screening metric.

Paying for Whose Performance? Teacher Incentive Pay and the Black–White Test Score Gap

2021 ◽  
pp. 016237372110014
Author(s):  
Andrew J. Hill ◽  
Daniel B. Jones
Keyword(s):  
Test Scores ◽  
Test Score ◽  
Performance Pay ◽  
Incentive Pay ◽  
Difference In Differences ◽  
Teacher Incentives ◽  
Test Score Gap ◽  
Before And After ◽  
White Test ◽  
Matched Data

Teacher performance pay is often introduced with the goal of reducing gaps in test scores across groups, yet little is known about how well they achieve this aim. We ask, “Do test score-based teacher incentives impact the Black–White test score gap?” Using student–teacher matched data and a difference-in-differences approach in which the performance of a teacher’s students before and after the policy is compared, we find that performance pay increases the conditional Black–White gap. The effect is particularly evident when bonuses are large, consistent with a causal response to performance pay.

Teacher’s Corner: Toward a Coherent View of Reliability in Test Theory

1997 ◽  
Vol 22 (4) ◽  
pp. 478-484
Author(s):  
Heng Li ◽  
Howard Wainer
Keyword(s):  
Internal Consistency ◽  
Test Scores ◽  
General Framework ◽  
Test Theory ◽  
Mathematical Framework ◽  
Individual Character ◽  
The Individual ◽  
Reliability Coefficients

Reliability of test scores, as estimated through measures of internal consistency, has been characterized mathematically in many ways that appear, on the surface at least, to be very dissimilar to one another. In this essay we provide a general mathematical framework that specializes to four different reliability coefficients. Through consideration of this general framework it becomes easier to convey to students both the individual character of the different formulations of reliability and the extent of their underlying similarity. In addition to providing a coherent view of reliability, the unified formula is also found to be a convenient vehicle for introducing more specialized topics, such as the Kaiser-Guttman rule.

scholarly journals Case-based learning in integrated management of neonatal and childhood illness

2019 ◽  
Vol 6 (3) ◽  
pp. 1163
Author(s):  
Sundaram Kartikeyan ◽  
Aniruddha A. Malgaonkar
Keyword(s):  
Gender Differences ◽  
Test Scores ◽  
Test Score ◽  
Integrated Management ◽  
Childhood Illness ◽  
Cognitive Domain ◽  
Post Test ◽  
Case Based Learning ◽  
The Difference ◽  
Case Based

Background: This complete-enumeration, before-and-after type of study (without controls) was conducted on 61 third-year medical students at Rajiv Gandhi Medical College, Thane, Maharashtra state to study the difference in cognitive domain scores after attending lecture-based learning (by a pre-test) and after attending case-based learning (by a post-test).Methods: After approval from the institutional ethics committee, the purpose of the study was explained to third-year medical students and written informed consent was obtained. After curriculum-based lectures on integrated management of neonatal and childhood Illness, a pre-test was administered wherein each student was asked to fill up case sheets for five case scenarios. The maximum marks obtainable were 10 marks per case (total 50 marks).  Case-based learning was conducted in two sub-groups comprising 31 and 30 randomly assigned students by the same faculty and students in each sub-group were exposed to identical case scenarios. The post-test was conducted using case scenarios and case sheets that were identical to that of the pre-test.Results: The overall mean score increased and the difference between the case-wise pre-test and post-test scores of both female (n=35) and male (n=26) students was highly significant (p <0.00001). However, the gender differences in pre-test score (Z=1.038; p=0.299) and post-test score were not significant (Z=0.114; p=0.909).Conclusions: Using case scenarios augmented the cognitive domain scores of participating students and the gender differences in scores were not statistically significant. The post-test scores showed higher variability. Remedial educational interventions would be required for students who obtained low scores in the post-test.

Abstract 245: Assessing Patient Knowledge and Satisfaction Following Stroke Video Education

2018 ◽  
Vol 11 (suppl_1) ◽  
Author(s):  
Sarah Tisel ◽  
Abigail Rieman ◽  
Matthew Hodges ◽  
Kelly Gwathmey
Keyword(s):  
High School ◽  
Patient Education ◽  
Knowledge Acquisition ◽  
Preliminary Data ◽  
Test Scores ◽  
Test Score ◽  
Stroke Patients ◽  
Highly Educated ◽  
Video Group ◽  
Video Education

Objective: To create a stroke education video and study its impact in stroke clinic with regards to patient satisfaction and knowledge acquisition. Background: Excellent medical care includes providing patient education, but most clinics do not emphasize this. We are exploring the use of video education in the stroke clinic, as this patient population may particularly benefit from secondary stroke prevention teaching. Methods: Ischemic stroke patients coming for routine hospital follow-up were enrolled and randomized to either watch an educational stroke video or receive standard care. Patients were stratified by education level, with one group having completed high school or less, and the other having completed any post high school training. Both groups took a pre- and post-visit knowledge test as well as a post-visit satisfaction survey. We hypothesized that knowledge acquisition, judged by improvement in test score, and satisfaction scores would both be greater in the video group. Test scores were expected to positively correlate with satisfaction. Results: Forty patients were enrolled to date. Preliminary data demonstrated patients were positive (n=15) or neutral (n=5) about the video. Both groups were highly satisfied with their visit, and a two-tailed t-test demonstrated no difference in satisfaction between groups (p=0.89). A linear regression showed a trend for the highly educated patients in the video group having improved test scores after the visit (p=0.069). Further enrollment of patients is needed to better assess this. In both groups, there was a correlation between post-test scores and satisfaction scores (R=0.37, p=0.03). Conclusions: Based on this preliminary data, stroke patients enjoy video education in clinic. However, as patients in both groups were highly satisfied, it remains unclear whether video education increases overall satisfaction. Highly educated patients may bennefit from video education, but further enrollment will clarify this. A positive correlation between post-visit test score and satisfaction indicates that clinics should prioritize patient education.

Discussion of “on State Education Statistics”: A Difficulty with Regression Analyses of Regional Test Score Averages

1985 ◽  
Vol 10 (4) ◽  
pp. 326-333 ◽  
Author(s):  
Paul R. Rosenbaum ◽  
Donald B. Rubin
Keyword(s):  
High School ◽  
Test Scores ◽  
Test Score ◽  
Theoretical Calculations ◽  
Regression Coefficients ◽  
Regression Analyses ◽  
State Education ◽  
Education Statistics ◽  
High School And Beyond

The Department of Education’s table “State Education Statistics” reports mean test scores by state and mean resource inputs by state. The means are calculated from quite different groups of students, a process we call inconsistent aggregation. We investigate the bias in regression coefficients caused by inconsistent aggregation, first using theoretical calculations, and then by artificially aggregating data from the High School and Beyond sample.

scholarly journals School Competition and Efficiency with Publicly Funded Catholic Schools

2010 ◽  
Vol 2 (4) ◽  
pp. 150-176 ◽  
Author(s):  
David Card ◽  
Martin D Dooley ◽  
A. Abigail Payne
Keyword(s):  
Standard Deviation ◽  
Simple Model ◽  
Test Scores ◽  
Catholic Schools ◽  
Test Score ◽  
Sixth Grade ◽  
School Systems ◽  
Competitive Pressure ◽  
Publicly Funded ◽  
School Competition

We study competition between two publicly funded school systems in Ontario, Canada: one that is open to all students, and one that is restricted to children of Catholic backgrounds. A simple model of competition between the competing systems predicts greater effort by school managers in areas with more Catholic families who are willing to switch systems. Consistent with this insight, we find significant effects of competitive pressure on test score gains between third and sixth grade. Our estimates imply that extending competition to all students would raise average test scores in sixth grade by 6 percent to 8 percent of a standard deviation. (JEL I21, I22, H75, Z12)

Sign in / Sign up

Export Citation Format

Share Document