The Promises and Perils of Diagnostic Labels

Last registered on February 01, 2021


Trial Information

General Information

The Promises and Perils of Diagnostic Labels
Initial registration date
September 19, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 21, 2020, 11:26 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
February 01, 2021, 10:40 PM EST

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Stanford University

Other Primary Investigator(s)

PI Affiliation
PI Affiliation
PI Affiliation
Stanford University

Additional Trial Information

On going
Start date
End date
Secondary IDs
Diagnostic labels are commonly used in educational and medical fields. Although research has shown that diagnostic labels can have adverse consequences on individual outcomes, there remain fundamental, unresolved questions in this literature: (1) To what extent does the introduction of diagnostic labels affect outcomes for a group on average? (2) To what extent do labels improve average group outcomes at the expense of certain subgroups of individuals? (3) How do the effects of diagnostic labels for groups and individuals depend on the proportion of other individuals who are also labelled in the group? To answer these questions, we propose a large-scale cluster randomized controlled trial among approximately 5,000 first-grade schoolchildren in 195 schools in Russia. Schools were randomly assigned to one of two treatment arms, where teachers received information about the performance of their students (a) without a corresponding diagnostic label for their students’ performance or (b) with a label for students as being “basic” / “proficient” in their academic performance or “developing” / “mature” in their behavioral performance. Results will inform the functions and tradeoffs of diagnostic labels for groups and individuals.
External Link(s)

Registration Citation

Abdurakhmanova, Elen et al. 2021. "The Promises and Perils of Diagnostic Labels." AEA RCT Registry. February 01.
Experimental Details


In the control group schools, psychometricians will provide teachers with numeric measures of student cognitive (academic) and non-cognitive (behavioral) performance (on a scale between 0 to 100) on the baseline assessments. This information is provided as part of an ongoing partnership with the regional governments to equip teachers with more information about their students.

In the treatment group schools, psychometricians will not only provide teachers with the numeric measures of student cognitive and non-cognitive performance but also diagnostic labels of student performance. Specifically, students across the sample who scored below 50 in their academic assessment are labeled as “basic” rather than “proficient,” and students in the sample who scored below 50 in their behavioral assessment are labeled as “developing” rather than “mature.” These assessments and labels are designed and pre-tested by Russian academics to be easily interpretable by teachers. For instance, teachers were invited to focus groups to offer feedback on different labels, and these labels were the most easily understood.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Students will participate in math, reading, and behavioral assessments (created by a team of trained psychometricians). Performance on the standardized math and reading assessments will be the primary outcome variables in this study.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Students will be asked to report on their sense of belonging in the classroom to understand the potentially stigmatizing effects of diagnostic labels. Teachers will also be given a roster of their students and asked to describe, for each student, their perception of their academic and behavioral performance. Moreover, teachers will be asked to estimate the time and effort they directed toward each student. We collect teacher perceptions and self-reported effort toward individual students as important mediators to explore in the study.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will conduct a cluster randomized controlled trial (RCT) involving 5,392 first grade schoolchildren (mean age of children = 7.4 years, proportion female = 0.50) across 195 schools in two regions of Russia. The vast majority of the 195 schools had one first grade class and teacher, but some schools had multiple first grade classes, each with its own teacher. Students and teachers from all first-grade classes were requested to participate in the study (for a total of 288 classes and teachers). All teachers participated in both a baseline and follow-up survey questionnaire. All students, in both baseline and follow-up, also participated in standardized cognitive and non-cognitive assessments produced by trained psychometricians.

Schools were randomized to one of two treatment arms in the RCT. Sample strata or blocks were created by placing the four schools with the closest mean math scores in a strata. Altogether, this resulted in 49 strata. Schools were then randomly allocated within these strata to one of two different treatment conditions.
Experimental Design Details
Randomization Method
Randomization done in office by a computer (Stata)
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
195 schools
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
97 schools in control group; 98 schools in treatment group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on our blocked/stratified randomization design, we can estimate minimum detectable effect sizes (MDESs) of 0.08 SDs for math and 0.11 SDs for reading (alpha=0.05, power=0.8, ICC=0.000 for math and 0.064 for reading, R-squared=0.45).

Institutional Review Boards (IRBs)

IRB Name
Higher School of Economics (Moscow)
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials