Research talk:VisualEditor's effect on newly registered editors/May 2015 study/Work log/2015-09-30

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Wednesday, September 30, 2015[edit]

Today I want to look at the short, mid and long-term survival trends of editors in the experimental conditions. I was recently working on a similar analysis for an experiment with the Teahouse. I'm going to replicate the same methodology here. If you want to read the details, see Research_talk:Teahouse_long_term_new_editor_retention/Work_log/2015-09-28.

The experiment ended on June 4th, 2015, so that means I have nearly 4 months to work with. I want to look at the following trial and survival periods:

  • 1 week, 1 month
  • 1 month, 1 month
  • 2 months, 1 month

So now the query:

SELECT
    user_id,
    SUM(revisions_1_to_2_weeks) AS revisions_1_to_2_weeks,
    SUM(revisions_1_to_2_months) AS revisions_1_to_2_months,
    SUM(revisions_2_to_3_months) AS revisions_2_to_3_months
FROM (
    (SELECT 
        user_id,
        SUM(
            rev_timestamp IS NOT NULL AND 
            DATEDIFF(rev_timestamp, user_registration) BETWEEN 7 AND 14
        ) AS revisions_1_to_2_weeks,
        SUM(
            rev_timestamp IS NOT NULL AND 
            DATEDIFF(rev_timestamp, user_registration) BETWEEN 30 AND 60
        ) AS revisions_1_to_2_months,
        SUM(
            rev_timestamp IS NOT NULL AND 
            DATEDIFF(rev_timestamp, user_registration) BETWEEN 60 AND 90
        ) AS revisions_2_to_3_months
    FROM staging.ve2_experimental_users as user
    INNER JOIN user USING (user_id)
    LEFT JOIN revision ON 
        rev_user = user_id AND
        rev_timestamp >= DATE_FORMAT(
            DATE_ADD(user_registration, INTERVAL 7 DAY), 
            "%Y%m%d%H%i%S"
        )
    GROUP BY 1)
    UNION
    (SELECT 
        user_id,
        SUM(
            ar_timestamp IS NOT NULL AND 
            DATEDIFF(ar_timestamp, user_registration) BETWEEN 7 AND 14
        ) AS revisions_1_to_2_weeks,
        SUM(
            ar_timestamp IS NOT NULL AND 
            DATEDIFF(ar_timestamp, user_registration) BETWEEN 30 AND 60
        ) AS revisions_1_to_2_months,
        SUM(
            ar_timestamp IS NOT NULL AND 
            DATEDIFF(ar_timestamp, user_registration) BETWEEN 60 AND 90
        ) AS revisions_2_to_3_months
    FROM staging.ve2_experimental_users AS user
    INNER JOIN user USING (user_id)
    LEFT JOIN archive ON 
        ar_user = user_id AND
        ar_timestamp >= DATE_FORMAT(
            DATE_ADD(user_registration, INTERVAL 21 DAY), 
            "%Y%m%d%H%i%S"
        )
    GROUP BY 1)
) user_span_revisions
GROUP BY user_id;

Here's a sample of the output:

user_id revisions_1_to_2_weeks  revisions_1_to_2_months revisions_2_to_3_months
2532<snip>        6       0       0
2532<snip>        0       0       0
2532<snip>        0       0       0
2532<snip>        0       0       0
2532<snip>        4       1       0
2532<snip>        2       0       0
2532<snip>        0       0       0
2532<snip>        0       0       0
2532<snip>        0       0       0

1+ edits survival[edit]

First, I'll consider an editor "surviving" if they make at least 1 edit in the survival period.

bucket 1 to 2 weeks.k 1 to 2 months.k 2 to 3 months.k n 1 to 2 weeks.p 1 to 2 months.p 2 to 3 months.p
control 321 294 211 13464 0.02384135 0.02183601 0.01567142
experimental 311 327 230 13507 0.0230251 0.02420967 0.01702821
Chi^2 tests
> prop.test(bucket.survival$revisions_1_to_2_weeks.k, bucket.survival$n)

	2-sample test for equality of proportions with continuity correction

data:  bucket.survival$revisions_1_to_2_weeks.k out of bucket.survival$n
X-squared = 0.1623, df = 1, p-value = 0.6871
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.002868681  0.004501194
sample estimates:
    prop 1     prop 2 
0.02384135 0.02302510 

> prop.test(bucket.survival$revisions_1_to_2_months.k, bucket.survival$n)

	2-sample test for equality of proportions with continuity correction

data:  bucket.survival$revisions_1_to_2_months.k out of bucket.survival$n
X-squared = 1.585, df = 1, p-value = 0.208
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.006027302  0.001279978
sample estimates:
    prop 1     prop 2 
0.02183601 0.02420967 

> prop.test(bucket.survival$revisions_2_to_3_months.k, bucket.survival$n)

	2-sample test for equality of proportions with continuity correction

data:  bucket.survival$revisions_2_to_3_months.k out of bucket.survival$n
X-squared = 0.6897, df = 1, p-value = 0.4063
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.004457761  0.001744186
sample estimates:
    prop 1     prop 2 
0.01567142 0.01702821

Looks like the VE cohort is a bit ahead, but the difference is not significant.

5+ edits survival[edit]

Let's try considering survival to only be legitimate if the editor saves at least 5 edits in the survival period.

bucket 1 to 2 weeks.k 1 to 2 months.k 2 to 3 months.k n 1 to 2 months.p 1 to 2 weeks.p 2 to 3 months.p
control 108 116 83 13464 0.008615567 0.00802139 0.006164587
experimental 102 129 79 13507 0.009550603 0.00755164 0.005848819
Chi^2 tests
> prop.test(bucket.survival5$revisions_1_to_2_weeks.k, bucket.survival5$n)

	2-sample test for equality of proportions with continuity correction

data:  bucket.survival5$revisions_1_to_2_weeks.k out of bucket.survival5$n
X-squared = 0.1366, df = 1, p-value = 0.7117
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.001702440  0.002641941
sample estimates:
    prop 1     prop 2 
0.00802139 0.00755164 

> prop.test(bucket.survival5$revisions_1_to_2_months.k, bucket.survival5$n)

	2-sample test for equality of proportions with continuity correction

data:  bucket.survival5$revisions_1_to_2_months.k out of bucket.survival5$n
X-squared = 0.5552, df = 1, p-value = 0.4562
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.003273534  0.001403462
sample estimates:
     prop 1      prop 2 
0.008615567 0.009550603 

> prop.test(bucket.survival5$revisions_2_to_3_months.k, bucket.survival5$n)

	2-sample test for equality of proportions with continuity correction

data:  bucket.survival5$revisions_2_to_3_months.k out of bucket.survival5$n
X-squared = 0.0659, df = 1, p-value = 0.7974
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.001602756  0.002234292
sample estimates:
     prop 1      prop 2 
0.006164587 0.005848819 

Again we don't see significance, but unlike above, we don't see a clear advantage for either group. --Halfak (WMF) (talk) 18:32, 30 September 2015 (UTC)