Description Format Note Source References

Dataset with raw data from Snijders and Bosker (2012) containing data from 4106 pupils attending 216 schools. This dataset includes all pupils and schools with missing data.

`brandsma`

is a data frame with 4106 rows and 14 columns:

`sch`

School number

`pup`

Pupil ID

`iqv`

IQ verbal

`iqp`

IQ performal

`sex`

Sex of pupil

`ses`

SES score of pupil

`min`

Minority member 0/1

`rpg`

Number of repeated groups, 0, 1, 2

`lpr`

language score PRE

`lpo`

language score POST

`apr`

Arithmetic score PRE

`apo`

Arithmetic score POST

`den`

Denomination classification 1-4 - at school level

`ssi`

School SES indicator - at school level

This dataset is constructed from the raw data. There are a few differences with the data set used in Chapter 4 and 5 of Snijders and Bosker:

All schools are included, including the five school with missing values on

`langpost`

.Missing

`denomina`

codes are left as missing.Aggregates are undefined in the presence of missing data in the underlying values. Variables

`ses`

,`iqv`

and`iqp`

are in their original scale, and not globally centered. No aggregate variables at the school level are included.There is a wider selection of original variables. Note however that the source data contain an even wider set of variables.

Constructed from `MLbook_2nded_total_4106-99.sav`

from
https://www.stats.ox.ac.uk/~snijders/mlbook.htm by function
`data-raw/R/brandsma.R`

Brandsma, HP and Knuver, JWM (1989), Effects of school and classroom characteristics on pupil progress in language and arithmetic. International Journal of Educational Research, 13(7), 777 - 788.

Snijders, TAB and Bosker RJ (2012). Multilevel Analysis, 2nd Ed. Sage, Los Angeles, 2012.

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