Seven experiments (total N = 3,509) and a large field data set (N = 1,820,671) demonstrate that time periods of equal duration are not always perceived as equivalent. The authors find that periods feel longer when they span more time categories (e.g., hour, month). For example, periods like 1:45 p.m.–3:15 p.m. and March 31–April 6 (boundary-expanded) feel longer than, say, 1:15 p.m.–2:45 p.m. and April 2–April 8 (boundary-compressed). Reflecting this, participants anticipated completing more work during boundary-expanded periods than during equivalent boundary-compressed periods. This effect appears to result from the salience and placement of time boundaries. Consequently, participants preferred scheduling pleasant activities for boundary-expanded periods and unpleasant activities for boundary-compressed periods. Moreover, participants were willing to pay more to avoid—and required more money to endure—a long wait when that wait was presented as boundary-expanded. Finally, data from more than 1.8 million rideshare trips suggest that consumers are more likely to choose independent rides (e.g., UberX) when they are boundary-compressed when the alternative shared option (e.g., UberPool) is boundary-expanded. Together, our studies reveal that time periods feel longer when they span more boundaries and that this phenomenon shapes consumers’ scheduling and purchasing decisions. 

Time Periods Feel Longer When They Span More Category Boundaries: Evidence from the Lab and the Field

Subject Confusion and Task Non-Completion: Methodological Insights from an Artefactual Field Experiment in India

Kayu Sengon merupakan material yang sering digunakan untuk bahan perkakas dan konstruksi bangunan yang disediakan dengan cara penggergajian kayu. Prediksi hasil penggergajian Sengon dilakukan untuk mengetahui jumlah hasil yang bisa didapat yang biasanya dilakukan dengan cara menebak atau melalui perhitungan manual. Metode tersebut membutuhkan pengalaman, waktu lama, dan susah untuk dilakukan sehingga perlu solusi untuk membantu mempercepat proses prediksi dengan menerapkan data mining. Penelitian ini dilakukan untuk melakukan prediksi hasil penggergajian kayu Sengon yang dilakukan dengan langkah akuisisi data, dataset, model regresi (Poisson, Support Vector, dan Ridge), dan evaluasi. Hasil penelitian menunjukkan bahwa regresi Poisson merupakan model terbaik karena memiliki error terkecil dengan Mean Absolute Error (MAE) 0,514 dan Root Mean Square Error (RMSE) 0,672 sehingga model ini tepat diterapkan pada sistem penggergajian Sengon.

/pdf/perbandingan-performa-regresi-menggunakan-poisson-support-3p4d4oya.pdf

Perbandingan Performa Regresi Menggunakan Poisson, Support Vector, dan Ridge pada Prediksi Hasil Penggergajian Sengon

In the behavioral and experimental economics literature, very few experiments measure and analyze subject confusion or the completion of laboratory tasks. In this paper, we examine pre-task quiz performance in our artefactual field experiment with an adolescent subject pool in India and find that females have a lower degree of confusion in comprehending task instructions as compared to males. Furthermore, economic status is negatively associated with subject comprehension and completion of incentivized experimental tasks. Academic ability is negatively associated with subject confusion and positively associated with task completion. Finally, we find a positive association between pre-task quiz performance and the completion of incentivized tasks. This suggests that unincentivized pre-task quiz scores may act as a signal for potential non-completion of incentivized tasks in experiments, particularly with non-standard subject pools. 

Subject Confusion and Task Non-Completion: Methodological Insights from an Artefactual Field Experiment with Adolescents in India

Early racial disparities in COVID-19 vaccination rates have been attributed primarily to personal vaccine attitudes and behavior. Little attention has been paid to the possibility that inequitable vaccine distribution may have contributed to racial disparities in vaccine uptake when supplies were most scarce. We test the hypothesis that scarce vaccines were distributed inequitably using the shipping addresses of 385,930 COVID-19 vaccine doses distributed in the first 17 weeks of Pennsylvania's Phase 1 rollout (December 14, 2020 through April 12, 2021). All shipments we analyze were allocated via the Federal Retail Pharmacy Program, a public-private partnership coordinated by the Centers for Disease Control and Prevention.Overall, White people had an average of 81.4% more retail pharmacy program doses shipped to their neighborhoods than did Black people. Regression models reveal that weekly vaccine allocations determined by pharmacy chains-rather than initial shipment and administration site decisions requiring state and federal approval-drove these effects. All findings remained consistent after controlling for neighborhood differences in income, population density, insurance coverage, number of pharmacies, and other social determinants of health.Our findings suggest that the private distribution of scarce public resources should be assessed for racial impact, regulated as public resources, and monitored continuously. 

Racial disparities in Phase 1 COVID-19 vaccine shipments to Neighborhood sites in Pennsylvania by the Federal Retail Pharmacy Program

When analyzing count data (such as number of questions answered correctly), psychologists often use Poisson regressions. We show through simulations that violating the assumptions of a Poisson distribution even slightly can lead to false positive rates more than doubling, and illustrate this issue with a study that finds a clearly spurious but highly significant connection between seeing the color blue and eating fish candies. In additional simulations we test alternative methods for analyzing count-data and show that these generally do not suffer from the same inflated false positive rate, nor do they result in much higher false negatives in situations where Poisson would be appropriate.

/pdf/poisson-regressions-a-little-fishy-qdbrjn1l91.pdf

Poisson Regressions: A Little Fishy

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Poisson Regressions: A Little Fishy