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Modeling
Models are of great importance in the natural and social sciences for investigating phenomena and making causal inferences. A wide range of subjects has been studied using models, including biology, communications, economics, education, geology, political science, psychology, and sociology, among others. Such models differ in many ways, but they all aim to represent reality indirectly: The goal is to scrutinize reality through an artificial system that resembles, in some important aspects, the properties of the world that people are attempting to understand. By focusing on some aspects of the target phenomena, models are able to provide manageable representations that can eventually lead to further understanding of those phenomena.
Representing human behavior by modeling is by no means easy, however, and can be a challenge to various disciplines. Researchers are faced with a number of modeling problems. For instance, much social science research is based on nonexperimental data, such as obtained by survey or quasi-experiment. Moreover, unlike studies in natural sciences, variables to be modeled are unavoidably measured with error, and failing to take measurement error into consideration yields unreliable statistical inferences. In addition, measurements in social sciences may involve categorical and nominal variables, such as gender and ethnicity, besides the quantitative and continuous variables. Finally, with longitudinal data gaining popularity, new modeling methods are needed to take repeated measures into account. This entry provides an overview of how modeling is applied in different disciplines and daily lives and introduces as examples four commonly used models in social sciences, namely categorical models, linear models, nonlinear models, and structural equation models.
When to Use Models
Models are everywhere in our daily lives, and understanding models can help people to become intelligent citizens, think in a clearer way, better understand data, and better strategize and plan. As the British statistician George E. P. Box famously noted, all models are wrong, but some are useful. It makes sense that models can be wrong, given that they are simplifications and abstractions of reality, but how can models be useful for people to work in a better way?
Modeling is nowadays the language not only of the academy but also of a variety of other domains. People from every discipline are using models to enable them to achieve their goals. Financial analysts on Wall Street are using models to forecast a corporation’s profitability or determine the benefits of a merger. Biologists are using models of the brain to investigate where there are pathways between the neurons. Sociologists use models to identity effects of people’s actions and behaviors, and psychologists model to measure people’s attitudes. In political science, people use the spatial voting model to predict voting choices based on candidates’ and voters’ respective political orientations. Linguists also use models to understand the structure of a language under study. And in daily life, it is not uncommon to receive advice from one acquaintance and the exact opposite advice from another. One possible way to adjudicate between such opposing counsel is by constructing models that can stipulate the conditions under which it would be appropriate to follow each recommended course of action, respectively. Although the actual application of modeling differs from discipline to discipline, in a general sense, modeling is useful for people to think about and to make sense of the world, and perhaps understand how the world could work in a better way.
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