Data cleaning hints and tips Felicity Clemens Stata

Introduction § Data cleaning – one of the most time consuming jobs of all!

Contents 1) Introduction to the first datasets 2) Identifying and removing duplicates – by

The study § A case-control study carried across 3 central European countries § Exposure

Identifying duplicates in a dataset § This can be done automatically (using the duplicates

The merge command A necessary command in data management of most big studies There

Identifying a moving target § Scenario: we have data for each town giving the

Identifying a moving target (2) rescode y 1990 y 1991 y 1992 1010113 65

Identifying a moving target (3) We will use the forval loop to examine the

Summary § Identifying duplicates – can be done by hand or automatically using the

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Data cleaning: hints and tips Felicity Clemens Stata Users’ Group meeting London, 17 & 18 th May 2005 Felicity Clemens 18 May 2005

Introduction § Data cleaning – one of the most time consuming jobs of all! § Many ways of attacking the same problem when using Stata § The talk will describe some common problems and propose possible solutions § These are mostly reminders! Felicity Clemens 18 May 2005

Contents 1) Introduction to the first datasets 2) Identifying and removing duplicates – by hand 3) Merging data and uses of the merge command 4) Generating a moving target variable Felicity Clemens 18 May 2005

The study § A case-control study carried across 3 central European countries § Exposure of interest: exposure to chemicals in the environment § Outcome of interest: cancer Felicity Clemens 18 May 2005

Identifying duplicates in a dataset § This can be done automatically (using the duplicates set of commands) § We will demonstrate a manual method of identifying duplicates § Two different possibilities: § The same data have been entered on more than one occasion; Felicity Clemens 18 May 2005

The merge command A necessary command in data management of most big studies There are many different uses of the merge command. We look at two of them: § Simple merge on id § Multiple merge on id Felicity Clemens 18 May 2005

Identifying a moving target § Scenario: we have data for each town giving the chemical concentration for each year between 1982 and 2002 § Problem: we need to identify the year counting backwards from 2002 in which the chemical changed from its 2002 level § Why? We need to overwrite the 2002 value with a new value, and overwrite backwards until the value changed Felicity Clemens 18 May 2005

Identifying a moving target (2) rescode y 1990 y 1991 y 1992 1010113 65 32 32 1010114 41 41 41 1010115 78 23 23 1010116 44 44 44 1010117 82 82 29 1010118 25 25 25 1010119 12 12 6 1010120 40 12 7 Felicity Clemens 18 May 2005

Identifying a moving target (3) We will use the forval loop to examine the relationship between each year’s observed value and the observed value for the previous year Felicity Clemens 18 May 2005

Summary § Identifying duplicates – can be done by hand or automatically using the “duplicates” set of commands § Use of the merge command – to merge on a specific variable, to multiply merge datasets § Generating a moving target variable – the use of the “forval” loop Felicity Clemens 18 May 2005