Identification of multi-valued treatment effects with unobserved heterogeneity

In this paper, we establish the sufficient conditions for identifying treatment effects on continuous outcomes in endogenous and multi-valued discrete treatment settings with unobserved heterogeneity. We employ the monotonicity assumption for multi-valued discrete treatments and instruments, and our identification condition has clear economic interpretation. Our result contrasts with related work by Chernozhukov and Hansen (2005) with regard to this. In addition, we identify the local treatment effects in multi-valued treatment settings and derive a closed-form expression of the identified treatment effects. We provide examples to illustrate the usefulness of our result

Joint diagnostic test of regression discontinuity designs: multiple testing problem

Current diagnostic tests for regression discontinuity (RD) design face a multiple testing problem. We find a massive over-rejection of the identifying restriction among empirical RD studies published in top-five economics journals. Each test achieves a nominal size of 5%; however, the median number of tests per study is 12. Consequently, more than one-third of studies reject at least one of these tests and their diagnostic procedures are invalid for justifying the identifying assumption. We offer a joint testing procedure to resolve the multiple testing problem. Our procedure is based on a new joint asymptotic normality of local linear estimates and local polynomial density estimates. In simulation studies, our joint testing procedures outperform the Bonferroni correction

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Stress Management App Protocol

This study investigates the effect of a stress management intervention delivered via a mobile app on affect among university students. Participants are randomly assigned to either an intervention group or a wait-list control group. The intervention group uses the app to select and practice stress management activities (e.g., music, relaxation, breathing) over a three-week period. Changes in affect (positive and negative affect) are assessed using the PANAS at pre-intervention, post-intervention, and three-week follow-up

Effects of an App-Based Stress-Management Intervention on Affect in University Students: A Randomized Controlled Trial

Registration timing & linkage — This is a retrospective registration that supersedes our prior preregistrations (JP/EN) which mistakenly stated the target sample size as N = 48. The correct target/sample size is N = 56 (28 intervention / 28 waitlist). Trial registration: UMIN-CTR UMIN000058224; public record: https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000066563 Data collection: May 20, 2023 – July 16, 2023. We have accessed the dataset for quality checks; analyses conceived after data access will be labeled exploratory; confirmatory analyses follow the plan below. Prior records (kept for transparency): Japanese (https://osf.io/2vepw), English (https://osf.io/gf957) 1. Title Effects of an App-Based Stress-Management Intervention on Affect in University Students: A Randomized Controlled Trial 2. Principal Investigator and Contact • PI: Ayumi Fusejima • Affiliation: Department of Psychological Information Design, College of Media and Information, Kanazawa Institute of Technology (KIT) • Email: [email protected] 3. Objective To examine whether an app-based stress-management intervention improves affect among university students, focusing on positive affect, negative affect, and affect balance (PA − NA). 4. Study Design • Design: Randomized controlled trial, parallel groups (intervention vs. waitlist control) • Masking: None (open-label) 5. Participants • Population: Students enrolled at Kanazawa Institute of Technology • Age: ≥ 18 years • Target sample size (corrected): N = 56 (Intervention n = 28; Waitlist n = 28) o Note: A prior figure (N = 48; 24/24) was incorrect and has been corrected to the above. • Exclusion criterion: Insufficient ability to understand Japanese 6. Intervention • Intervention group: For 3 weeks, participants self-select and practice stress-management behaviors offered in the mobile app (e.g., music listening, breathing exercises, relaxation) in daily life. • Waitlist control: No intervention during the main study period; app access is provided after the follow-up assessment. 7. Outcomes • Primary outcomes: Changes in negative affect, positive affect, and affect balance (PA − NA) measured with PANAS at baseline (pre-intervention), post-intervention, and follow-up. • Secondary outcomes: Types/frequency of in-app stress-management behaviors selected and participants’ evaluations of those behaviors; dropout/retention metrics. 8. Analysis Plan • Primary analysis sets: Intention-to-treat (ITT), with a per-protocol/completers (PPS) sensitivity analysis. • Statistical methods: Between-group comparisons using mixed-effects models (and/or repeated-measures ANOVA where appropriate). o Factors: Group (intervention vs. waitlist) × Time (baseline, post, follow-up) o Effect of interest: Group × Time interaction 9. Ethics • Approved by the Research Ethics Committee of Kanazawa Institute of Technology (Approval No. 2212002). • Written informed consent is obtained from all participants. 10. Trial Registration and Key Dates • Registry: UMIN-CTR • Registration ID: UMIN000058224 • Registry URL: https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000066563 • Trial start date: May 20, 2023 • Follow-up completion: July 16, 2023 11. Deviations / Corrections from Prior Records • Target sample size correction: Prior preregistrations stated N = 48; the correct target/sample size is N = 56 (28/28). • The study design, primary outcomes, and main analysis plan remain unchanged. 12. Data / Code Availability De-identified data, analysis scripts, and materials will be made available on OSF as applicable and consistent with ethical approval and participant consent. Links will be added to this record upon release. 2025/10/16 Data &Code availability. The minimal, de-identified dataset (data_clean/ rawdate_long_minmal.csv, data_clean/ rawdate_wide_minmal.csv), the codebook (metadata/codebook.csv), and quality flags (data_clean/quality_flags.csv) are deposited under the Files tab of this registration (DOI: 10.17605/OSF.IO/5RN9S). The SPSS syntax used in the analyses is also provided: code/ code_itt.sps (primary ITT mixed-effects models) and code/code_pss.sps (per-protocol repeated-measures ANOVA). License: CC BY 4.0 for data and documentation; analysis code under MIT