Tarefa causcibench_real.q174

Veredito

Pergunta feita ao analista

What is the effect of WhatsApp messages on individuals's knowledge about Covid19?

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Verificações

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Trajetória

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1. 0Início

   What is the effect of WhatsApp messages on individuals's knowledge about Covid19?

   bowles_countering (agent)

   The data aims to test whether trusted WhatsApp messages can counter COVID-19 misinformation in Zimbabwe. Partnering with Internews and Kubatana, the researchers crafted short weekly messages: Week 1 emphasized transmission and social distancing, Week 2 debunked fake cures. At the WhatsApp broadcast list level, timing was randomized: treatment groups received messages on Monday, control groups on Saturday. To measure effects, a survey was sent on Thursday. Knowledge was assessed through factual questions, and behavior through a list experiment, where respondents received either a short list (only non-sensitive activities) or a long list (the same activities plus a sensitive one: visiting others during lockdown). Broadcast lists were blocked in groups of four, with random assignment across timing (treatment vs. control) and list length (short vs. long). Data variables ResponseId: identifier for the responses from the individuals Behavior: number of activities the respondent participated in, coded from 0 to 5 Id: identifier for individuals participating List: binary variable, equals 1 if the WhatsApp broadcast list is assigned to the long experimental list qualtrics: binary variable indicating response mode (1 if Qualtrics, 0 if WhatsApp) T: treatment indicator, equals 1 if the WhatsApp broadcast list is assigned to the treatment group female: binary variable, equals 1 if the respondent is female c_Q4: survey question on factual knowledge about COVID-19 transmission or prevention c_Q5: survey question on factual knowledge about misinformation or fake cures Knowledge: z-score of the respondent's answers to the factual questions c_Q4_1: sub-item of Q4 (first option, coded 1 if correct, 0 otherwise) c_Q4_3: sub-item of Q4 (third option, coded 1 if correct, 0 otherwise) c_Q4_4: sub-item of Q4 (fourth option, coded 1 if correct, 0 otherwise) c_Q4_5: sub-item of Q4 (fifth option, coded 1 if correct, 0 otherwise) block: randomization block (grouping of broadcast lists created at similar times) length_subscribe: number of months the WhatsApp broadcast list had been active Week: indicator for intervention week (1 = distancing message, 2 = misinformation/cures message) response_rate: fraction of weekly responses coming from WhatsApp per person Week_id: numeric identifier for the week urban: binary variable, equals 1 if the individual lived in an urban district

   file 3107fc57c0d67588eccecf4788cf3600ab114f22fa27694bebd9c1892a8e5049 · frame —
2. 1Turno do modelo
   call_a97f1e75naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "bowles_countering",
       "x": [
         "T"
       ],
       "y": "Knowledge"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_7424e73fdecidido pelo modeloreexecutável

   Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "bowles_countering",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "T"
     ],
     "y": "Knowledge"
   }

   Figuras call_7424e73f

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.T Coefficient on T in the regression of Knowledge	0,25743	0.2574298736710165	0,06776770.06776773399135474	0,124421 – 0,390439

   Diagnósticos

   • n_obs = 864864
   • k = 22
   • r2 = 0,016410.01641000918762292
   • df_resid = 862862
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": -0.03479209051058359,
         "ci_low": -0.22207798977787796,
         "coef": -0.12843504014423077,
         "p": 0.007241311564740516,
         "se": 0.04771080805074566,
         "t": -2.6919485414631015,
         "term": "Intercept"
       },
       {
         "ci_high": 0.3904389498155496,
         "ci_low": 0.12442079752648338,
         "coef": 0.2574298736710165,
         "p": 0.00015566231101922333,
         "se": 0.06776773399135474,
         "t": 3.7987085963927507,
         "term": "T"
       }
     ]
   }

   • 4 rows were dropped because one of 'Knowledge', 'T' was missing

   Digests dos dados

   bowles_countering ce0afeaa1f0f9b2b7f6ca76a07d3288fa4145d73a3a3d4de3128c4e148972b63

   sha256 do resultado

   bbe34f6ded7fa43aff13dac7df815ceec47e5529eb39df470121e74271a14560

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_1f4acc37naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa ingênua (infer.ols): coef.T = 0,2574.",
       "caveats": [],
       "figure_refs": [
         "call_7424e73f:coef.T"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   0,2574

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa ingênua (infer.ols): coef.T = 0,2574.

   Figuras citadas: call_7424e73f:coef.T · Parada: answered

   Figura da resposta: call_7424e73f:coef.T = 0,257430.2574298736710165 (se 0,06776770.06776773399135474)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.2574298737 vs 0.269

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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