RT Article
T1 Visualizing Lives: New Pathways for Analyzing Life Course Trajectories
JF Journal of quantitative criminology
VO 16
IS 2
SP 255
OP 281
A1 Maltz, Michael D.
A2 Mullany, Jacqueline M.
LA English
YR 2000
UL https://krimdok.uni-tuebingen.de/Record/1764280784
AB The goal of statistical analysis is to find patterns in data. Most statistical methods rely on analyzing the effect of the same set of variables on the population under study, i.e., nomothetic analysis. Therefore, when data are collected in the social sciences, most often they are put in a framework that resembles a spreadsheet: each row represents a separate individual, and each column represents a separate characteristic (or variable) that pertains to that individual. However, not all individuals in the study are affected by the same set of variables: each individual may have his/her own individual set of relevant variables, suggesting that methods be developed that consider them individually, i.e., idiographic analysis. Moreover, lives are lived chronologically, and are often best described in narrative form. These narratives usually have to be condensed, or abridged in other ways, in order to fit the data framework and permit what one might call ``algorithmic analysis”. Each set of methods has its advantage: nomothetic methods generate general laws that apply to all, while idiographic methods trace the putative causal relationships that are unique to each individual. This paper describes another data collection and analytic framework, one that (a) is chronological; (b) recognizes that different people may have experienced entirely different events and thus may need different ``variables” to understand their behavior; (c) recognizes that, even if people experience similar events, they may have entirely different reactions to them; and (d) can be studied (and patterns inferred) using an exploratory graphical analysis that is more free-form than algorithmic analysis. Examples of this type of analysis used in different medical and criminal justice contexts are given, and suggested directions of research in this area are described.
K1 exploratory data analysis
K1 Data visualization
K1 Life Course
K1 graphical analysis
K1 Longitudinal Analysis
DO 10.1023/A:1007572707667