Principles of Data Analysis

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The structure of panel data has three dimensions. First, the respective examination units (n) and a matrix of dependent and independent variables (y,x) are completely analogous to a cross-sectional design. Second, the dimension of time (t), whereby a distinction is made between two data formats for panel data structures - “wide” or “long” (with wide format the variable matrix is indexed with the dimension of time and with long format the respective examination units). Regardless of the selected data format, when using panel data with several survey waves, the data matrices often do not contain complete information due to the panel mortality of individual survey units or because data from new panel members are only collected at a later point in time. In both cases, the term “unbalanced panel data” is used. In contrast, the classical panel data structure, on the other hand, is “balanced”, i.e., as many observations of dependent and independent variables are available for all study units as there are waves of data collection. Social science panel data often show a data structure characterized by many investigation units (large n) as well as, in relation to it, few waves and therefore measuring time (small t). When data from a panel study are available, even descriptive forms of data analysis are often of particular interest, since the identification of changes in a variable over time and the corresponding separation of interindividual and intraindividual changes can represent important social facts, particularly in the case of generalizable samples. It is of social scientific interest whether a constant 15% proportion of people whose income is below the poverty risk level is repeatedly found in the same person over time, or whether there was a even balance of increases and decreases in poverty risks and only half of the population was permanently exposed to the risk. The choice of complex analysis methods for panel data depends first and foremost on the respective measurement level of the dependent and independent variables, but also on whether they are time-constant variables (such as gender or migration background) or time-invariant variables. The statistical analysis models of panel data range from structural equation models, various regression models, event analysis, sequence data analysis, latent growth models to causal analyses using matching methods. A particular advantage of panel data is that the chronological sequence of changes can be modelled and calculated and the problem of unobserved heterogeneity, which is often encountered in the social sciences, can be significantly reduced, at least in comparison with cross-sectional data.

Cross-Sectional Data Structure (CS)

Cross-sectional data is a type of data that observes many subjects at the same point in time. Each person is assigned a row in the dataset and is only included once in such a dataset. By merging cross-sectional SOEP data across waves, you obtain a dataset in wide-format.

Row	ID	wave	sex	income
1	1	2015	m	1500
2	2	2015	m	1000
3	6	2015	f	2000
4	8	2015	m	5500

Data Structure in “Wide” Format (wide)

The SOEP data are available with different data structures. In the wide format, a respondent’s repeated responses are displayed in a single row and each response in a separate column. Each column represents a variable. We provide four datasets in the wide format: ppath, phrf, hpath, hhrf.

Row	ID	sex	income2015	income2016	income2017
1	1	m	1500	1500	2000
2	2	m	1000	1200	1200
3	6	f	2000	2000	2000
4	8	m	5500	6000	6500

Data Structure in “Long” Format (long)

The long format is a condensed and user-friendly dataset structure for longitudinal section analysis. Here, each person has one line per survey year. This means that you do not have several datasets for the different waves, but one dataset in which all survey waves are represented. A person can appear more than once in such a dataset. In the long format, one line describes a person-year combination.

Row	ID	syear	sex	income
1	1	2015	m	1500
2	1	2016	m	1500
3	1	2017	m	2000
4	2	2015	m	1000
5	2	2016	m	1200
6	2	2017	m	1200
7	6	2015	f	2000
8	6	2016	f	2000
9	6	2017	f	2000

In addition to the classic long format where one row in the dataset describes a person-year combination, there are also datasets that describe a longer period or a whole life, but only appear uniquely in the dataset without a survey year. These data sets can contain longitudinal information, but are constant over time. These time-constant data sets may include, for example, information on biological parents or employment history up to a certain age.

Row	ID	father_id	mother_id
1	20	18	19
2	21	18	19
3	35	34	35
4	36	34	35
5	37	34	35

Data Structure in Spell Format (spell)

In the strict sense of the word, spell data are about time periods with a defined start and end. When handling spell data it is necessary to take potential censoring into account. Censoring denotes that the beginning (left censored) or ending (right censored) of a spell is imprecise because of missing information or the beginning or ending of a spell is outside of the period of observation. It is quite conceivable that a person has only one spell over a given period, such as a male who is full-time employed. For a ten year period, there may be just the one spell “full-time employed”. In panel data, the same person would have 10 observations, one per year. A person may have many spells over a time period, and even have overlapping spells, like working part-time and receiving a disability pension. Spell data are useful for looking at stays in a certain state, and transitions in and out of that state.

Row	ID	spellnr	spelltype	begin	end	censored
1	1	1	Retired	1983	2007	left and right censored
2	1	2	Housewife/husband	1983	1984	left censored
3	1	3	Housewife/husband	1994	1994	uncensored
4	1	4	Housewife/husband	1998	1998	uncensored
5	2	1	Full-Time Employment	1984	1984	left censored
6	2	2	Full-Time Employment	1985	1985	uncensored

Last change: Jun 06, 2024