Estimating Causal Effects Using School-Level Data Sets

2007 ◽  
Vol 36 (4) ◽  
pp. 187-198 ◽  
Author(s):  
Elizabeth A. Stuart
Keyword(s):  
Regression Analysis ◽  
Comparison Group ◽  
Causal Effects ◽  
School Level ◽  
Data Sets ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Level Data ◽  
Careful Design

Education researchers, practitioners, and policymakers alike are committed to identifying interventions that teach students more effectively. Increased emphasis on evaluation and accountability has increased desire for sound evaluations of these interventions; and at the same time, school-level data have become increasingly available. This article shows researchers how to bridge these two trends through careful use of school-level data to estimate the effectiveness of particular interventions. The author provides an overview of common methods for estimating causal effects with school-level data, including randomized experiments, regression analysis, pre–post studies, and nonexperimental comparison group designs. She stresses the importance of careful design of nonexperimental studies, particularly the need to compare units that were similar before treatment assignment. She gives examples of analyses that use school-level data and concludes with advice for researchers.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Dampness and student-reported social climate: two multilevel mediation models

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Eerika Finell ◽  
Asko Tolvanen ◽  
Juha Pekkanen ◽  
Timo Ståhl ◽  
Pauliina Luopa
Keyword(s):  
Indoor Air ◽  
Social Climate ◽  
School Level ◽  
Data Sets ◽  
Student Relationships ◽  
Teacher Student Relationships ◽  
Teacher Student ◽  
Mediational Models ◽  
The Relationship ◽  
Perceptions Of School

Abstract Background Little previous research has analysed the relationship between schools’ indoor air problems and schools’ social climate. In this study, we analysed a) whether observed mould and dampness in a school building relates to students’ perceptions of school climate (i.e. teacher-student relationships and class spirit) and b) whether reported subjective indoor air quality (IAQ) at the school level mediates this relationship. Methods The data analysed was created by merging two nationwide data sets: survey data from students, including information on subjective IAQ (N = 25,101 students), and data from schools, including information on mould and dampness in school buildings (N = 222). The data was analysed using multilevel mediational models. Results After the background variables were adjusted, schools’ observed mould and dampness was not significantly related to neither student-perceived teacher-student relationships nor class spirit. However, our mediational models showed that there were significant indirect effects from schools’ observed mould and dampness to outcome variables via school-level subjective IAQ: a) in schools with mould and dampness, students reported significantly poorer subjective IAQ (standardised β = 0.34, p < 0.001) than in schools without; b) the worse the subjective IAQ at school level, the worse the student-reported teacher-student relationships (β = 0.31, p = 0.001) and class spirit (β = 0.25, p = 0.006). Conclusions Problems in a school’s indoor environment may impair the school’s social climate to the degree that such problems decrease the school’s perceived IAQ.

scholarly journals Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shirley X. Liao ◽  
Lucas Henneman ◽  
Cory Zigler
Keyword(s):  
American Statistical Association ◽  
Treatment Patterns ◽  
Average Treatment Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Medicare Beneficiaries ◽  
Finite Sample ◽  
Treatment Assignment ◽  
Power Plant Emissions ◽  
Plant Emissions

AbstractMarginal structural models (MSM) with inverse probability weighting (IPW) are used to estimate causal effects of time-varying treatments, but can result in erratic finite-sample performance when there is low overlap in covariate distributions across different treatment patterns. Modifications to IPW which target the average treatment effect (ATE) estimand either introduce bias or rely on unverifiable parametric assumptions and extrapolation. This paper extends an alternate estimand, the ATE on the overlap population (ATO) which is estimated on a sub-population with a reasonable probability of receiving alternate treatment patterns in time-varying treatment settings. To estimate the ATO within an MSM framework, this paper extends a stochastic pruning method based on the posterior predictive treatment assignment (PPTA) (Zigler, C. M., and M. Cefalu. 2017. “Posterior Predictive Treatment Assignment for Estimating Causal Effects with Limited Overlap.” eprint arXiv:1710.08749.) as well as a weighting analog (Li, F., K. L. Morgan, and A. M. Zaslavsky. 2018. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association 113: 390–400, https://doi.org/10.1080/01621459.2016.1260466.) to the time-varying treatment setting. Simulations demonstrate the performance of these extensions compared against IPW and stabilized weighting with regard to bias, efficiency, and coverage. Finally, an analysis using these methods is performed on Medicare beneficiaries residing across 18,480 ZIP codes in the U.S. to evaluate the effect of coal-fired power plant emissions exposure on ischemic heart disease (IHD) hospitalization, accounting for seasonal patterns that lead to change in treatment over time.

New Evidence on Eastern Europe's Pollution Progress

2003 ◽  
Vol 3 (1) ◽  
Author(s):  
Matthew E Kahn
Keyword(s):  
Sulfur Dioxide ◽  
Carbon Dioxide Emissions ◽  
National Level ◽  
Data Sets ◽  
Local Public Good ◽  
Data Set ◽  
The Czech Republic ◽  
Level Data ◽  
Good Improvement ◽  
New Evidence

Abstract Under communism, Eastern Europe's cities were significantly more polluted than their Western European counterparts. An unintended consequence of communism's decline is to improve urban environmental quality. This paper uses several new data sets to measure these gains. National level data are used to document the extent of convergence across nations in sulfur dioxide and carbon dioxide emissions. Based on a panel data set from the Czech Republic, Hungary and Poland, ambient sulfur dioxide levels have fallen both because of composition and technique effects. The incidence of this local public good improvement is analyzed.

Evaluation of Network-Level Data Collection Variability and its Influence on Pavement Evaluation Utilizing Random Forest Method

2021 ◽  
pp. 036119812098043
Author(s):  
Xiaoyang Jia ◽  
Mark Woods ◽  
Hongren Gong ◽  
Di Zhu ◽  
Wei Hu ◽  
...  
Keyword(s):  
Data Collection ◽  
High Variability ◽  
Pavement Management ◽  
Data Sets ◽  
State Highway ◽  
Condition Evaluation ◽  
Level Data ◽  
Pavement Evaluation ◽  
Data Variability ◽  
Parallel Tests

The use of pavement condition data to support maintenance and resurfacing strategies and justify budget needs becomes more crucial as more data-driven approaches are being used by the state highway agencies (SHAs). Therefore, it is important to understand and thus evaluate the influence of data variability on pavement management activities. However, owing to a huge amount of data collected annually, it is a challenge for SHAs to evaluate the influence of data collection variability on network-level pavement evaluation. In this paper, network-level parallel tests were employed to evaluate data collection variability. Based on the data sets from the parallel tests, classification models were constructed to identify the segments that were subject to inconsistent rating resulting from data collection variability. These models were then used to evaluate the influence of data variability on pavement evaluation. The results indicated that the variability of longitudinal cracks was influenced by longitudinal lane joints, lateral wandering, and lane measurement zones. The influence of data variability on condition evaluation for state routes was more significant than that for interstates. However, high variability of individual metrics may not necessarily lead to high variability of combined metrics.

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

2013 ◽  
Vol 1 (1) ◽  
pp. 135-154 ◽  
Author(s):  
Peter M. Aronow ◽  
Joel A. Middleton
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Covariate Adjustment ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Unbiased Estimators ◽  
Average Treatment

AbstractWe derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

scholarly journals Primary schools and the amplification of social differences in child mental health: a population-based cohort study

2017 ◽  
Vol 72 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Louise Marryat ◽  
Lucy Thompson ◽  
Helen Minnis ◽  
Philip Wilson
Keyword(s):  
Mental Health ◽  
Primary Schools ◽  
Demographic Data ◽  
Population Based ◽  
School Level ◽  
Social Emotional ◽  
Level Data ◽  
Mental Health Difficulties ◽  
Early Primary ◽  
Additional Support

BackgroundThis paper examines socioeconomic inequalities in mental health at school entry and explores changes in these inequalities over the first 3 years of school.MethodsThe study utilises routinely collected mental health data from education records and demographic data at ages 4 and 7 years, along with administrative school-level data. The study was set in preschool establishments and schools in Glasgow City, Scotland. Data were available on 4011 children (59.4%)at age 4 years, and 3166 of these children were followed at age 7 years (46.9% of the population). The main outcome measure was the teacher-rated Goodman’s Strengths and Difficulties Questionnaire (4–16 version) at age 7 years, which measures social, emotional and behavioural difficulties.ResultsChildren living in the most deprived area had higher levels of mental health difficulties at age 4 years, compared with their most affluent counterparts (7.3%vs4.1% with abnormal range scores). There was a more than threefold widening of this disparity over time, so that by the age of 7 years, children from the most deprived area quintile had rates of difficulties 3.5 times higher than their more affluent peers. Children’s demographic backgrounds strongly predicted their age 7 scores, although schools appeared to make a significant contribution to mental health trajectories.ConclusionsAdditional support to help children from disadvantaged backgrounds at preschool and in early primary school may help narrow inequalities. Children from disadvantaged backgrounds started school with a higher prevalence of mental health difficulties, compared with their more advantaged peers, and this disparity widened markedly over the first 3 years of school.

scholarly journals Penguatan Konsep Kombinatorika Siswa SMAN 1 Muara Batu Kabupaten Aceh Utara dalam Menghadapi KSN-K Bidang Matematika 2021

2021 ◽  
Vol 2 (2) ◽  
pp. 174
Author(s):  
Herizal Herizal
Keyword(s):  
Data Analysis ◽  
Community Service ◽  
Direct Observation ◽  
Learning Process ◽  
School Level ◽  
Level Data ◽  
National Science ◽  
Service Activity ◽  
The Subject

This community service activity aimed to strengthen students’ understanding of  the combinatorics concepts in facing the regency-level of National Science Competition (KSN) in field of mathematics in 2021. The activity was carried out in March-April 2021 for six meetings in the form of training/coaching. The training used both discovery and drilling methods. The location of the activity was at SMAN 1 Muara Batu, North Aceh Regency with four students as the subject who have been selected at the school level and selected to participate in the KSN at the regency level. Data analysis was carried out qualitatively by direct observation to observe the improvement of the students’ comprehension during the learning process. The result obtained was an improvement of the students’ understanding of combinatorics topic. It can be seen in solving problems, the students are able to determine what concepts will be used and able to solve several KSN questions on combinatorics topic.

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