Estimate an effect of intervention on every Viva Insights metric in input file by applying single-group Interrupted Time-Series Analysis (ITSA)
Source:R/create_ITSA.R
create_ITSA.Rdr lifecycle::badge('experimental')
This function implements ITSA method described in the paper 'Conducting interrupted time-series analysis for single- and multiple-group comparisons', Ariel Linden, The Stata Journal (2015), 15, Number 2, pp. 480-500
This function further requires the installation of 'sandwich', 'portes', and
'lmtest' in order to work. These packages can be installed from CRAN using
install.packages().
Arguments
- data
Person Query as a dataframe including date column named
Date. This function assumes the data format is MM/DD/YYYY as is standard in a Viva Insights query output.- before_start
Start date of 'before' time period in MM/DD/YYYY format as character type. Before time period is the period before the intervention (e.g. training program, re-org, shift to remote work) occurs and bounded by before_start and before_end parameters. Longer period increases likelihood of achieving more statistically significant results. Defaults to earliest date in dataset.
- before_end
End date of 'before' time period in MM/DD/YYYY format as character type.
- after_start
Start date of 'after' time period in MM/DD/YYYY format as character type. After time period is the period after the intervention occurs and bounded by after_start and after_end parameters. Longer period increases likelihood of achieving more statistically significant results. Defaults to date after before_end.
- after_end
End date of 'after' time period in MM/DD/YYYY format as character type. Defaults to latest date in dataset.
- ac_lags_max
maximum lag for autocorrelation test. Default is 7
- return
String specifying what output to return. Defaults to "table". Valid return options include:
'plot': return a list of plots.'table': return data.frame with estimated models' coefficients and their corresponding p-values You should look for significant p-values in beta_2 to indicate an immediate treatment effect, and/or in beta_3 to indicate a treatment effect over time
Details
This function uses the additional package dependencies 'sandwich' and 'lmtest'. Please install these separately from CRAN prior to running the function.
As of May 2022, the 'portes' package was archived from CRAN. The dependency
has since been removed and dependent functions Ljungbox() incorporated into
the wpa package.
See also
Other Flexible Input:
period_change()
Examples
# Returns summary table
create_ITSA(
data = sq_data,
before_start = "11/03/2019",
before_end = "12/15/2019",
after_start = "12/29/2019",
after_end = "1/26/2020",
ac_lags_max = 7,
return = "table")
#> metric_name beta_2 beta_3 beta_2_pvalue
#> 1 Workweek_span 1.788 0.972 0.477
#> 2 Meetings_with_skip_level 0.010 0.008 0.623
#> 3 Meeting_hours_with_skip_level 0.020 0.000 0.159
#> 4 Generated_workload_email_hours 1.151 0.776 0.434
#> 5 Generated_workload_email_recipients 17.236 11.739 0.440
#> 6 Generated_workload_instant_messages_hours 0.079 0.049 0.388
#> 7 Generated_workload_instant_messages_recipients 4.222 2.819 0.434
#> 8 Generated_workload_call_hours 0.918 -0.374 0.204
#> 9 Generated_workload_call_participants 0.961 -0.415 0.201
#> 10 Generated_workload_calls_organized 0.493 -0.213 0.201
#> 11 External_network_size 0.289 0.164 0.006
#> 12 Internal_network_size 8.445 4.335 0.000
#> 13 Networking_outside_company 0.073 0.048 0.055
#> 14 Networking_outside_organization 0.008 0.001 0.000
#> 15 After_hours_meeting_hours 0.167 0.087 0.405
#> 16 Open_1_hour_block -1.311 -0.546 0.337
#> 17 Open_2_hour_blocks -0.729 -0.341 0.403
#> 18 Total_focus_hours -1.449 -0.709 0.406
#> 19 Low_quality_meeting_hours 1.087 0.530 0.327
#> 20 Total_emails_sent_during_meeting 1.492 0.772 0.311
#> 21 Meetings 1.431 0.690 0.384
#> 22 Meeting_hours 1.271 0.545 0.335
#> 23 Conflicting_meeting_hours 1.033 0.404 0.228
#> 24 Multitasking_meeting_hours 0.192 0.109 0.317
#> 25 Redundant_meeting_hours__lower_level_ 0.019 0.001 0.193
#> 26 Redundant_meeting_hours__organizational_ 0.634 0.309 0.279
#> 27 Time_in_self_organized_meetings 0.237 0.137 0.467
#> 28 Meeting_hours_during_working_hours 1.090 0.459 0.338
#> 29 Generated_workload_meeting_attendees 1.148 0.531 0.427
#> 30 Generated_workload_meeting_hours 0.742 0.447 0.492
#> 31 Generated_workload_meetings_organized 0.272 0.133 0.437
#> 32 Manager_coaching_hours_1_on_1 -0.004 0.012 0.906
#> 33 Meetings_with_manager 0.715 0.372 0.395
#> 34 Meeting_hours_with_manager 0.632 0.332 0.359
#> 35 Meetings_with_manager_1_on_1 0.042 0.013 0.240
#> 36 Meeting_hours_with_manager_1_on_1 0.036 0.013 0.343
#> 37 After_hours_email_hours 0.178 0.125 0.481
#> 38 Emails_sent 5.452 3.619 0.424
#> 39 Email_hours 1.211 0.804 0.422
#> 40 Working_hours_email_hours 0.255 0.162 0.378
#> 41 After_hours_instant_messages 0.034 0.021 0.410
#> 42 Instant_messages_sent 8.267 5.076 0.380
#> 43 Instant_Message_hours 0.080 0.049 0.379
#> 44 Working_hours_instant_messages 0.046 0.028 0.356
#> 45 After_hours_collaboration_hours 0.345 0.212 0.444
#> 46 Collaboration_hours 2.482 1.348 0.380
#> 47 Collaboration_hours_external 0.382 0.309 0.496
#> 48 Working_hours_collaboration_hours 1.345 0.620 0.346
#> 49 After_hours_in_calls 0.234 -0.090 0.138
#> 50 Total_calls 1.452 -0.620 0.196
#> 51 Call_hours 1.934 -0.758 0.160
#> 52 Working_hours_in_calls 1.498 -0.649 0.164
#> 53 Layer -1.000 -1.000 -1.000
#> 54 HourlyRate -1.000 -1.000 -1.000
#> beta_3_pvalue AR_flag
#> 1 0.092 FALSE
#> 2 0.225 FALSE
#> 3 0.982 FALSE
#> 4 0.046 FALSE
#> 5 0.047 FALSE
#> 6 0.042 FALSE
#> 7 0.054 FALSE
#> 8 0.334 FALSE
#> 9 0.287 FALSE
#> 10 0.288 FALSE
#> 11 0.000 TRUE
#> 12 0.000 TRUE
#> 13 0.000 TRUE
#> 14 0.132 TRUE
#> 15 0.071 FALSE
#> 16 0.087 FALSE
#> 17 0.086 FALSE
#> 18 0.077 FALSE
#> 19 0.066 FALSE
#> 20 0.058 FALSE
#> 21 0.085 FALSE
#> 22 0.082 FALSE
#> 23 0.072 FALSE
#> 24 0.083 FALSE
#> 25 0.738 FALSE
#> 26 0.036 FALSE
#> 27 0.087 FALSE
#> 28 0.089 FALSE
#> 29 0.122 FALSE
#> 30 0.080 FALSE
#> 31 0.111 FALSE
#> 32 0.140 FALSE
#> 33 0.070 FALSE
#> 34 0.055 FALSE
#> 35 0.154 FALSE
#> 36 0.130 FALSE
#> 37 0.058 FALSE
#> 38 0.044 FALSE
#> 39 0.043 FALSE
#> 40 0.035 FALSE
#> 41 0.053 FALSE
#> 42 0.040 FALSE
#> 43 0.041 FALSE
#> 44 0.033 FALSE
#> 45 0.060 FALSE
#> 46 0.057 FALSE
#> 47 0.042 FALSE
#> 48 0.073 FALSE
#> 49 0.279 FALSE
#> 50 0.296 FALSE
#> 51 0.304 FALSE
#> 52 0.273 FALSE
#> 53 -1.000 FALSE
#> 54 -1.000 FALSE
#> error_warning
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#> 53 Warning: essentially perfect fit: summary may be unreliable; lm_train_success=FALSE
#> 54 Warning: essentially perfect fit: summary may be unreliable; lm_train_success=FALSE
# Returns list of plots
plot_list <-
create_ITSA(
data = sq_data,
before_start = "11/03/2019",
before_end = "12/15/2019",
after_start = "12/29/2019",
after_end = "1/26/2020",
ac_lags_max = 7,
return = 'plot')
# Extract a plot as an example
plot_list$Workweek_span
