Hello All,

I ran a mixed-effects model using country (50 countries) as a random intercept. The random intercept for country was statistically significant, and model fit improved significantly(as indicated by a lower -2 Log Likelihood).

The concern is 22 countries have only one respondent and 10 countries have 2 respondent, I was thinking to say: Even though the model looks better, but I will go with simple linear regression not mixed model due to sparse data (low sample size in lots of clusters)

As a note, I also grouped countries into 5 clusters to increase the sample size within each group. In this version, the random intercept was not significant, and the change in -2 Log Likelihood was not statistically meaningful.

Below is the frequency of each cluster/country:

country_1

1

country_2

1

country_3

1

country_4

24

country_5

1

country_6

2

country_7

1

country_8

5

country_9

22

country_10

2

country_11

1

country_12

1

country_13

20

country_14

12

country_15

1

country_16

1

country_17

1

country_18

1

country_19

1

country_20

2

country_21

2

country_22

1

country_23

2

country_24

1

country_25

6

country_26

1

country_27

3

country_28

4

country_29

32

country_30

5

country_31

1

country_32

6

country_33

15

country_34

10

country_35

1

country_36

1

country_37

18

country_38

2

country_39

3

country_40

2

country_41

1

country_42

3

country_43

6

country_44

8

country_45

1

country_46

2

country_47

2

country_48

7

country_49

6

country_50

18

country_51

5

country_52

1

country_53

1

country_54

9

country_55

3

total

290

Any input appreciated,

Thank you!

Bahar