Modifies survey weights for particular observations, adjusting other weights in the sampling unit or stratum to ensure that the overall sum of the weights remains unchanged (S.D. Langton).

### Options

`PRINT` = string tokens |
Controls printed output (`summary` ); default `summ` |
---|---|

`METHOD` = string tokens |
What to reweight over (`all` , `stratum` , `samplingunits` , `lowest` ); default `lowe` |

`WEIGHTS` = variate |
Initial weights |

`OUTWEIGHTS` = variate |
Final weights |

`STRATUMFACTOR` = factor |
Stratification factor; default `*` i.e. unstratified |

`OUTSTRATUMFACTOR` = factor |
Saves a modified stratification factor with the reweighted observations in their own stratum |

`SAMPLINGUNITS` = factor |
Factor indicating the primary sampling units; default `*` , i.e. single stage design |

`LABELS` = variate, text or factor |
Labels for each unit |

### Parameters

`OBSERVATIONS` = scalars, variates or texts |
Observations to reweight |
---|---|

`NEWWEIGHTS` = scalars or variates |
New weights (default inserts a missing value, indicating that the observation should be removed) |

### Description

Item non-response (i.e. a missing value for one question although valid responses are present for others) and outliers are two common problems in survey data. If the item response occurs entirely at random, one method of dealing with it is to analyse the question with a modified set of weights with the weight for the missing observation redistributed over the rest of the units in the stratum or sampling unit. This could be achieved by calculating the weights again from scratch, but it is often preferable to modify the existing weights variable. Similarly if the influence of outliers is reduced by giving them a reduced weight (see Lee 1995 for a discussion of this subject), the weights for the remaining observations must be adjusted to maintain the same sum of weights.

The units whose weights are to be adjusted are specified by the `OBSERVATIONS`

parameter. They may be specified in two different ways:

1) A list of observations whose weights need modifying may be supplied in one or more variates, scalars or texts. By default the units are identified by the unit number of the observation, but, if the LABELS option is set to a variate, factor or text, the values are matched against the labels. Multiple observations can be specified either as a list of scalars (or single-valued texts if appropriate), or by variates or texts with multiple values.

2) A variate of the same length as `WEIGHTS`

may be supplied, with the value one in the units whose weights need to be modifed. Other units should contain zeros.

By default the procedure assumes that the observations should have their weight set to missing so that they are excluded from analysis by `TABULATE`

or `SVTABULATE`

. Alternatively `NEWWEIGHTS`

can be used to specify the required weights to insert. This can be set to a scalar if the same weight is to be used for every unit specified by the corresponding `OBSERVATIONS`

variate, text or scalar. Alternatively, it can be set to a variate of the same length as the corresponding `OBSERVATIONS`

setting.

The `METHOD`

option specifies the level at which the weights are redistributed, so that, for example, setting `METHOD=stratum`

changes the other weights in the stratum containing the observation so that their total remains unchanged. If `METHOD`

is unset the procedure works to the lowest specified level, i.e. sampling units if these are specified, or otherwise the strata. If the stratification factor is also unspecified the redistribution takes place over all other observations.

Where reduced weights (typically 1.0) are allocated to outliers because they are genuine but not representative of the wider population, these units are often placed in their own stratum; the `OUTSTRATUMFACTOR`

option can be used to create such a suitable stratification factor.

Options: `PRINT`

, `METHOD`

, `WEIGHTS`

, `OUTWEIGHTS`

, `STRATUMFACTOR`

, `OUTSTRATUMFACTOR`

, `SAMPLINGUNITS`

, `LABELS`

.

Parameters: `OBSERVATIONS`

, `NEWWEIGHTS`

.

### Action with `RESTRICT`

Any restrictions are ignored.

### References

Lee, H. (1995). Outliers in Business Surveys. Chapter 26 of *Business Survey Methods* (ed. Cox, Binder, Hinnappa, Christianson, Colledge & Kott). Wiley, New York.

### See also

Procedures: `SVBOOT`

, `SVCALIBRATE`

, `SVGLM`

, `SVHOTDECK`

, `SVSAMPLE`

, `SVSTRATIFIED`

, `SVTABULATE`

, `SVWEIGHT`

.

Commands for: Survey analysis.

### Example

CAPTION 'SVREWEIGHT example',!t(\ 'Orkney oats data (Sampford, Table 5.1, page 61).',\ 'Stratified analysis as Table 6.1, page 73',\ 'The 12th data point is an outlier added to the dataset.');\ STYLE=meta,plain VARIATE Oats READ Oats 15 20 18 18 23 27 25 60 28 128 69 188 72 : FACTOR [LEVELS=3; VALUES=4(1,2),5(3)] Stratum VARIATE [VALUES=1...13] Row;DEC=0 TABLE [CLASS=Stratum; VALUES=12,12,12] N "note includes extra obs" " first restrict out the outlier to get results of Table 6.1, page 73 using SVTABULATE " RESTRICT Oats;Row.NE.12 SVSTRATIFIED [PRINT=summary,totals; STRATUMFACTOR=Stratum] Oats; NUNITS=N RESTRICT Oats " now repeat, by calculating weights and adjusting to remove outlier use modified N without extra obs" TABLE [CLASS=Stratum; VALUES=12,12,11] N SVWEIGHT [PRINT=summary; STRATUM=Stratum; NUNITS=N] OUTWEIGHTS=weights SVREWEIGHT [PRINT=summary; METHOD=*; WEIGHTS=weights; OUTWEIGHTS=modwt;\ STRATUM=Stratum] 12 "check estimate same as SVSTRATIFIED with outlier restricted out" SVTABULATE [PRINT=summary,totals; STRATUM=Stratum; WEIGHTS=modwt] Oats "do the same analysis, using the alternative way of specifying OBSERVATIONS" VARIATE [VALUES=11(0),1,0] Obs SVREWEIGHT [PRINT=summary; METHOD=*; WEIGHTS=weights; OUTWEIGHTS=modwt2;\ STRATUM=Stratum] Obs "check estimate same as SVSTRATIFIED with outlier restricted out" SVTABULATE [PRINT=summary,totals; STRATUM=Stratum; WEIGHTS=modwt2] Oats