Setting up the R session and Loading packages

# environment setup to run ordered logit properly
options(contrasts = rep("contr.treatment", 2))

This chunk loads all the packages to use

#packages for ordered logit
library(ordinal) # package for ordinal logit regression
library(brant) # brant test for the parallel assumption for ordered logit
library(MASS) # models that work with the brant test

library(tidyverse) # package for data cleaning and plotting
library(readxl) # package for reading excel file
library(broom) # extracting model summary as data frame
library(modelsummary) # deriving model tables
library(scales) # label percent
library(lubridate) # working with dates
library(marginaleffects) #to calculate marginal effects

Import the cleaned data

locations_coded <- read_csv("D:/Archive/Socialcare UK/data/locations_coded.csv")

Prepare the data for ordinal regression

#select relevant columns, rename and relabel 
locations_cleaned <- locations_coded %>% 
  # recode legal form types to be more readable / easier to present
  mutate(inherited = ifelse(inherited == "Y", TRUE, FALSE),
         rating = recode(rating, 
                         "Insufficient evidence to rate" = "NA",
                         "Requires improvement" = "Req improv"),
         date = ymd(publication_date)) %>% 
  
  # assign order in the rating levels
  mutate(rating = ordered(rating, levels = c("Inadequate","Req improv", "Good", "Outstanding")),
         form_spinout = case_when(form == "GOV" ~ "GOV",
                           form == "CIC" & spin_out == "TRUE" ~ "SP_CIC",
                           form == "CIC" & spin_out == "FALSE" ~ "NSP_CIC"),
         socialcare = ifelse(type == "Social Care Org", TRUE, FALSE)) %>% 
  
  # creating a new dummy variable for facility category
  mutate(year = year(date),
         during_covid = ifelse(year >= 2020, TRUE, FALSE),
         before_covid = ifelse(year <= 2019, TRUE, FALSE)) %>%

  # converting the ordinal variable to numerical 
  mutate(rating_num = case_when(rating == "Inadequate" ~ 1,
                                rating == "Req improv" ~ 2,
                                rating == "Good" ~ 3,
                                rating == "Outstanding" ~ 4)) 
# show first several rows of the data set derived 
head(locations_cleaned)
## # A tibble: 6 × 39
##   `Location ID` `Location ODS Code` `Location Name` `Care Home?` type           
##   <chr>         <chr>               <chr>           <chr>        <chr>          
## 1 1-1017570228  VN638               Bluebell Centre N            Social Care Org
## 2 1-1017570228  VN638               Bluebell Centre N            Social Care Org
## 3 1-1017570228  VN638               Bluebell Centre N            Social Care Org
## 4 1-1017570228  VN638               Bluebell Centre N            Social Care Org
## 5 1-1017570228  VN638               Bluebell Centre N            Social Care Org
## 6 1-1017570228  VN638               Bluebell Centre N            Social Care Org
## # ℹ 34 more variables: primary_cat <chr>, `Location Street Address` <chr>,
## #   `Location Address Line 2` <chr>, `Location City` <chr>,
## #   `Location Post Code` <chr>, `Location Local Authority` <chr>, region <chr>,
## #   `Location NHS Region` <chr>, `Location ONSPD CCG Code` <chr>,
## #   `Location ONSPD CCG` <chr>, `Location Commissioning CCG Code` <chr>,
## #   `Location Commissioning CCG Name` <chr>,
## #   `Service / Population Group` <chr>, domain <chr>, rating <ord>, …

drop GOV providers in extra primary_cad

cic_cats <- locations_cleaned %>%
  filter(form == "CIC") %>%
  distinct(primary_cat) %>% 
  pull(primary_cat)
locations_short <- locations_cleaned %>% 
  filter(primary_cat %in% cic_cats)
nrow(locations_cleaned)
## [1] 19931
nrow(locations_short)
## [1] 6807

Comparison table on Primary Inspection Category

datasummary(primary_cat ~ form, data = locations_cleaned, fmt = 0)
primary_cat CIC GOV
Acute hospital - NHS non-specialist 0 11948
Acute hospital - NHS specialist 0 944
Ambulance service 0 173
Community based adult social care services 402 2214
Community health - NHS & Independent 91 277
Community substance misuse 12 24
Dentists 0 12
GP Practices 470 492
Hospice services 6 18
Independent consulting doctors 47 0
Mental health - community & residential - NHS 0 47
Out of hours 72 12
Remote clinical advice 18 6
Residential social care 120 2490
Urgent care services & mobile doctors 24 12

whole model without control

model_order_overall <- clm(rating ~ form_spinout  + socialcare + region + inherited,
                data = filter(locations_short, domain == "Overall"),
                link = "logit")


model_order_safe <- clm(rating ~ form_spinout  + socialcare + region + inherited,
                data = filter(locations_short, domain == "Safe"),
                link = "logit")
model_order_effective <- clm(rating ~ form_spinout  + socialcare + region + inherited,
                data = filter(locations_short, domain == "Effective"),
                link = "logit")
model_order_caring <- clm(rating ~ form_spinout  + socialcare + region + inherited,
                data = filter(locations_short, domain == "Caring"),
                link = "logit")
model_order_well_led <- clm(rating ~ form_spinout + socialcare + region + inherited,
                data = filter(locations_short, domain == "Well-led"),
                link = "logit")
model_order_responsive <- clm(rating ~ form_spinout + socialcare + region + inherited,
                data = filter(locations_short, domain == "Responsive"),
                link = "logit")
ordinal_models <-
  modelsummary(
    list(
      "overall" = model_order_overall,
      "safe" = model_order_safe,
      "effective" = model_order_effective,
      "caring" = model_order_caring,
      "well-led" = model_order_well_led,
      "responsive" = model_order_responsive
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}")
ordinal_models
overall safe effective caring well-led responsive
Inadequate Req improv -6.647 -5.776 -5.351
(<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -2.306 -1.577 -2.410 -3.626 -1.896
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.794 5.100 4.029 3.427 2.793
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 0.296 0.309 -0.243 0.642 0.486 0.362
(0.155) (0.365) (0.504) (0.064) + (0.082) + (0.264)
form_spinoutSP_CIC 0.583 0.997 0.744 0.999 0.161 0.479
(0.033) * (0.053) + (0.126) (0.018) * (0.653) (0.252)
socialcareTRUE -0.322 0.264 -0.261 -0.174 -0.480 -0.317
(0.042) * (0.285) (0.366) (0.541) (0.023) * (0.212)
inheritedTRUE -0.767 -0.412 -0.695 -0.377 -0.193 -0.633
(<0.001) *** (0.179) (0.046) * (0.374) (0.478) (0.080) +
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 1527 1055 1051 1052 1057 1054
AIC 1714.0 898.6 781.1 701.7 1362.0 897.6
BIC 1794.0 973.0 850.5 771.1 1436.4 967.1
RMSE 2.33 2.13 1.18 1.29 2.32 1.33 
ordinal_models_exp <-
  modelsummary(
    list(
      "overall" = model_order_overall,
      "safe" = model_order_safe,
      "effective" = model_order_effective,
      "caring" = model_order_caring,
      "well-led" = model_order_well_led,
      "responsive" = model_order_responsive
    ),
    coef_omit = "region",
    exponentiate = T,
    statistic = "({p.value}) {stars}")
ordinal_models_exp
overall safe effective caring well-led responsive
Inadequate Req improv 0.001 0.003 0.005
(<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good 0.100 0.207 0.090 0.027 0.150
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 16.354 164.070 56.220 30.770 16.323
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 1.345 1.363 0.784 1.900 1.626 1.436
(0.155) (0.365) (0.504) (0.064) + (0.082) + (0.264)
form_spinoutSP_CIC 1.791 2.709 2.105 2.715 1.174 1.615
(0.033) * (0.053) + (0.126) (0.018) * (0.653) (0.252)
socialcareTRUE 0.725 1.302 0.770 0.841 0.619 0.729
(0.042) * (0.285) (0.366) (0.541) (0.023) * (0.212)
inheritedTRUE 0.464 0.662 0.499 0.686 0.825 0.531
(<0.001) *** (0.179) (0.046) * (0.374) (0.478) (0.080) +
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 1527 1055 1051 1052 1057 1054
AIC 1714.0 898.6 781.1 701.7 1362.0 897.6
BIC 1794.0 973.0 850.5 771.1 1436.4 967.1
RMSE 2.33 2.13 1.18 1.29 2.32 1.33

whole model with control

model_order_overall_covid <- clm(rating ~ form_spinout  + during_covid +
                                  socialcare + region + inherited,
                data = filter(locations_short, domain == "Overall"),
                link = "logit")

model_order_safe_covid <- clm(rating ~ form_spinout  + during_covid + socialcare + region + inherited,
                data = filter(locations_short, domain == "Safe"),
                link = "logit")
model_order_effective_covid <- clm(rating ~ form_spinout  + during_covid + socialcare + region + inherited,
                data = filter(locations_short, domain == "Effective"),
                link = "logit")
model_order_caring_covid <- clm(rating ~ form_spinout  + during_covid + socialcare + region + inherited,
                data = filter(locations_short, domain == "Caring"),
                link = "logit")
model_order_well_led_covid <- clm(rating ~ form_spinout + during_covid + socialcare + region + inherited,
                data = filter(locations_short, domain == "Well-led"),
                link = "logit")
model_order_responsive_covid <- clm(rating ~ form_spinout + during_covid + socialcare + region + inherited,
                data = filter(locations_short, domain == "Responsive"),
                link = "logit")
ordinal_models_covid <-
  modelsummary(
    list(
      "overall" = model_order_overall_covid,
      "safe" = model_order_safe_covid,
      "effective" = model_order_effective_covid,
      "caring" = model_order_caring_covid,
      "well-led" = model_order_well_led_covid,
      "responsive" = model_order_responsive_covid
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}"
  )
ordinal_models_covid
overall safe effective caring well-led responsive
Inadequate Req improv -7.314 -6.037 -5.644
(<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -2.861 -1.803 -2.546 -3.660 -2.132
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.572 5.024 3.967 3.399 2.696
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 0.373 0.489 -0.126 0.667 0.632 0.417
(0.072) + (0.161) (0.731) (0.055) + (0.023) * (0.198)
form_spinoutSP_CIC 0.581 0.931 0.714 0.983 0.122 0.451
(0.034) * (0.075) + (0.139) (0.020) * (0.733) (0.280)
during_covidTRUE -1.715 -1.062 -0.764 -0.222 -1.109 -0.447
(<0.001) *** (<0.001) *** (0.004) ** (0.464) (<0.001) *** (0.093) +
socialcareTRUE -0.217 0.339 -0.231 -0.165 -0.423 -0.301
(0.179) (0.176) (0.424) (0.563) (0.045) * (0.236)
inheritedTRUE -1.032 -0.589 -0.804 -0.406 -0.317 -0.689
(<0.001) *** (0.061) + (0.023) * (0.340) (0.251) (0.058) +
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 1527 1055 1051 1052 1057 1054
AIC 1630.7 876.9 775.2 703.1 1329.9 896.7
BIC 1716.0 956.3 849.5 777.5 1409.3 971.1
RMSE 2.33 2.12 1.18 1.29 2.31 1.33 
ordinal_models_covid_exp <-
  modelsummary(
    list(
      "overall" = model_order_overall_covid,
      "safe" = model_order_safe_covid,
      "effective" = model_order_effective_covid,
      "caring" = model_order_caring_covid,
      "well-led" = model_order_well_led_covid,
      "responsive" = model_order_responsive_covid
    ),
    coef_omit = "region",
    exponentiate = T,
    statistic = "({p.value}) {stars}"
  )
ordinal_models_covid_exp
overall safe effective caring well-led responsive
Inadequate Req improv 0.001 0.002 0.004
(<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good 0.057 0.165 0.078 0.026 0.119
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 13.099 151.977 52.848 29.930 14.826
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 1.452 1.631 0.882 1.948 1.881 1.517
(0.072) + (0.161) (0.731) (0.055) + (0.023) * (0.198)
form_spinoutSP_CIC 1.789 2.536 2.042 2.672 1.130 1.570
(0.034) * (0.075) + (0.139) (0.020) * (0.733) (0.280)
during_covidTRUE 0.180 0.346 0.466 0.801 0.330 0.640
(<0.001) *** (<0.001) *** (0.004) ** (0.464) (<0.001) *** (0.093) +
socialcareTRUE 0.805 1.404 0.794 0.848 0.655 0.740
(0.179) (0.176) (0.424) (0.563) (0.045) * (0.236)
inheritedTRUE 0.356 0.555 0.448 0.666 0.728 0.502
(<0.001) *** (0.061) + (0.023) * (0.340) (0.251) (0.058) +
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 1527 1055 1051 1052 1057 1054
AIC 1630.7 876.9 775.2 703.1 1329.9 896.7
BIC 1716.0 956.3 849.5 777.5 1409.3 971.1
RMSE 2.33 2.12 1.18 1.29 2.31 1.33

Below is additional analysis added 8/16/2023

Additional Analysis for Covid - The CIC subgroup regression, with short/comparable data

model_cic_overall_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Overall" & form == "CIC"),
                link = "logit")

model_cic_safe_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Safe" & form == "CIC"),
                link = "logit")
model_cic_effective_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Effective" & form == "CIC"),
                link = "logit")
model_cic_caring_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Caring" & form == "CIC"),
                link = "logit")
model_cic_well_led_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Well-led" & form == "CIC"),
                link = "logit")
model_cic_responsive_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Responsive" & form == "CIC"),
                link = "logit")
cic_models_covid <-
  modelsummary(
    list(
      "overall" = model_cic_overall_covid,
      "safe" = model_cic_safe_covid,
      "effective" = model_cic_effective_covid,
      "caring" = model_cic_caring_covid,
      "well-led" = model_cic_well_led_covid,
      "responsive" = model_cic_responsive_covid
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}"
  )
cic_models_covid
overall safe effective caring well-led responsive
Inadequate Req improv -6.701 -5.180 -4.887
(<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -3.262 -2.796 -3.151 -3.893 -2.265
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 1.418 2.939 2.463 1.772 1.579
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
during_covidTRUE -3.121 -1.725 -1.859 -0.563 -1.185 -1.947
(<0.001) *** (<0.001) *** (<0.001) *** (0.311) (0.004) ** (0.005) **
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 393 173 173 173 174 173
AIC 540.3 180.8 170.7 177.4 284.3 170.5
BIC 556.2 193.4 180.2 186.9 296.9 179.9
RMSE 2.54 2.21 1.31 1.50 2.51 1.45

Additional Analysis for Covid - The CIC subgroup regression, with altogether data

model_cic_overall_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Overall" & form == "CIC"),
                link = "logit")

model_cic_safe_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Safe" & form == "CIC"),
                link = "logit")
model_cic_effective_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Effective" & form == "CIC"),
                link = "logit")
model_cic_caring_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Caring" & form == "CIC"),
                link = "logit")
model_cic_well_led_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Well-led" & form == "CIC"),
                link = "logit")
model_cic_responsive_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Responsive" & form == "CIC"),
                link = "logit")
cic_models_covid2 <-
  modelsummary(
    list(
      "overall" = model_cic_overall_covid2,
      "safe" = model_cic_safe_covid2,
      "effective" = model_cic_effective_covid2,
      "caring" = model_cic_caring_covid2,
      "well-led" = model_cic_well_led_covid2,
      "responsive" = model_cic_responsive_covid2
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}"
  )
cic_models_covid2
overall safe effective caring well-led responsive
Inadequate Req improv -6.701 -5.180 -4.887
(<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -3.262 -2.796 -3.151 -3.893 -2.265
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 1.418 2.939 2.463 1.772 1.579
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
during_covidTRUE -3.121 -1.725 -1.859 -0.563 -1.185 -1.947
(<0.001) *** (<0.001) *** (<0.001) *** (0.311) (0.004) ** (0.005) **
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 393 173 173 173 174 173
AIC 540.3 180.8 170.7 177.4 284.3 170.5
BIC 556.2 193.4 180.2 186.9 296.9 179.9
RMSE 2.54 2.21 1.31 1.50 2.51 1.45

Additional Analysis for Covid - The Gov subgroup regression , with short/comparable data

model_gov_overall_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Overall" & form == "GOV"),
                link = "logit")

model_gov_safe_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Safe" & form == "GOV"),
                link = "logit")
model_gov_effective_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Effective" & form == "GOV"),
                link = "logit")
model_gov_caring_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Caring" & form == "GOV"),
                link = "logit")
model_gov_well_led_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Well-led" & form == "GOV"),
                link = "logit")
model_gov_responsive_covid <- clm(rating ~ during_covid,
                data = filter(locations_short, domain == "Responsive" & form == "GOV"),
                link = "logit")
gov_models_covid <-
  modelsummary(
    list(
      "overall" = model_gov_overall_covid,
      "safe" = model_gov_safe_covid,
      "effective" = model_gov_effective_covid,
      "caring" = model_gov_caring_covid,
      "well-led" = model_gov_well_led_covid,
      "responsive" = model_gov_responsive_covid
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}"
  )
gov_models_covid
overall safe effective caring well-led responsive
Req improv Good -2.688 -2.279 -2.811 -4.067 -2.175
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.782 5.047 3.658 2.817 2.699
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
during_covidTRUE -1.166 -0.828 -0.343 -0.080 -1.017 -0.029
(<0.001) *** (<0.001) *** (0.269) (0.820) (<0.001) *** (0.920)
Inadequate Req improv -5.982
(<0.001) ***
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 1134 882 878 879 883 881
AIC 1081.8 673.9 600.8 534.2 1052.8 727.3
BIC 1096.9 688.2 615.1 548.6 1072.0 741.7
RMSE 1.27 1.11 1.16 1.25 2.28 1.31

Additional Analysis for Covid - The Gov subgroup regression , with altogether data

model_gov_overall_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Overall" & form == "GOV"),
                link = "logit")

model_gov_safe_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Safe" & form == "GOV"),
                link = "logit")
model_gov_effective_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Effective" & form == "GOV"),
                link = "logit")
model_gov_caring_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Caring" & form == "GOV"),
                link = "logit")
model_gov_well_led_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Well-led" & form == "GOV"),
                link = "logit")
model_gov_responsive_covid2 <- clm(rating ~ during_covid,
                data = filter(locations_cleaned, domain == "Responsive" & form == "GOV"),
                link = "logit")
gov_models_covid2 <-
  modelsummary(
    list(
      "overall" = model_gov_overall_covid2,
      "safe" = model_gov_safe_covid2,
      "effective" = model_gov_effective_covid2,
      "caring" = model_gov_caring_covid2,
      "well-led" = model_gov_well_led_covid2,
      "responsive" = model_gov_responsive_covid2
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}"
  )
gov_models_covid2
overall safe effective caring well-led responsive
Inadequate Req improv -4.534 -4.101 -5.032 -7.507 -4.253
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -1.645 -1.128 -2.009 -4.229 -1.609
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.319 4.736 2.928 1.920 2.274
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
during_covidTRUE -1.266 -1.183 -0.813 -0.713 -1.149 -0.737
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 3373 3089 2747 3069 3087 3076
AIC 5358.3 4370.9 3330.9 2731.8 5051.1 4755.8
BIC 5382.8 4395.0 3354.6 2755.9 5075.3 4779.9
RMSE 2.35 2.14 2.25 2.40 2.36 2.35

Additional analysis with form vs. COVID with cross-tab, with short/comparable data

datasummary_crosstab(form_spinout * during_covid ~ rating, data = locations_short)
form_spinout during_covid Inadequate Req improv Good Outstanding All
GOV FALSE N 0 256 4072 190 4526
% row 0.0 5.7 90.0 4.2 100.0
TRUE N 3 151 813 52 1019
% row 0.3 14.8 79.8 5.1 100.0
NSP_CIC FALSE N 0 27 498 88 614
% row 0.0 4.4 81.1 14.3 100.0
TRUE N 3 43 158 11 215
% row 1.4 20.0 73.5 5.1 100.0
SP_CIC FALSE N 0 12 321 52 387
% row 0.0 3.1 82.9 13.4 100.0
TRUE N 3 16 26 1 46
% row 6.5 34.8 56.5 2.2 100.0
All N 9 505 5888 394 6807
% row 0.1 7.4 86.5 5.8 100.0

Additional analysis with form vs. COVID with cross-tab, with altogether data

datasummary_crosstab(form_spinout * during_covid ~ rating, data = locations_cleaned)
form_spinout during_covid Inadequate Req improv Good Outstanding All
GOV FALSE N 58 2083 11600 1085 15033
% row 0.4 13.9 77.2 7.2 100.0
TRUE N 180 988 2283 164 3636
% row 5.0 27.2 62.8 4.5 100.0
NSP_CIC FALSE N 0 27 498 88 614
% row 0.0 4.4 81.1 14.3 100.0
TRUE N 3 43 158 11 215
% row 1.4 20.0 73.5 5.1 100.0
SP_CIC FALSE N 0 12 321 52 387
% row 0.0 3.1 82.9 13.4 100.0
TRUE N 3 16 26 1 46
% row 6.5 34.8 56.5 2.2 100.0
All N 244 3169 14886 1401 19931
% row 1.2 15.9 74.7 7.0 100.0

The cross-tab shows that before COVID, CICs preform better, but during COVID, GOVs perform better.