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Judging Quality: Evaluative Criteria for Interpretive Empirical Research
Unformatted Document Text:  3 quality of a particular study. To illustrate this point, consider the following thought experiment. (I use a traditionally trained political scientist as my example because the interpretive tradition is not as well developed in political science as it is in some other social sciences. 2 ) Imagine that you are a traditionally trained political scientist, steeped in what might be called the “variables gestalt”—which encompasses, among other things, a commitment to measurement, hypothesis testing, and causal analysis. Setting aside epistemological and ontological presuppositions, as well as the possible variety of theoretical lens, when you sit down to read an empirical study you bring with you a set of standard expectations about the logic of research (as well as a developed, discipline- directed set of critical reading skills honed through training in your field). You are expecting to encounter independent and dependent variables (whose operationalization can be critically assessed); you are expecting to see causal reasoning and perhaps a causal model (the internal logic of which you know how to assess); you are expecting to find statistical analysis—be it a regression equation, ANOVA or some other technique (you might even turn immediately to the tables, before reading any of the text, in order to see what sort of analysis is involved); and you are trained to assess the appropriateness of the technique as a function of the measurement level of the data, to know whether underlying statistical assumptions are met, and so on. Over time, you have become quite practiced at reading and critiquing research conducted within this gestalt, and your application of 2 Research gestalts divide and cross disciplines in all of the social sciences (though the proportions vary dramatically across disciplines). One consequence is that, for example, a quantitatively trained sociologist may share more with a mathematician interested in applied statistics than with his feminist departmental colleague who, with her collaborator in cultural studies, examines the political meanings of rap music.

Authors: Schwartz-Shea, Peregrine.
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3
quality of a particular study. To illustrate this point, consider the following thought
experiment. (I use a traditionally trained political scientist as my example because the
interpretive tradition is not as well developed in political science as it is in some other
social sciences.
2
)
Imagine that you are a traditionally trained political scientist, steeped in what
might be called the “variables gestalt”—which encompasses, among other things, a
commitment to measurement, hypothesis testing, and causal analysis. Setting aside
epistemological and ontological presuppositions, as well as the possible variety of
theoretical lens, when you sit down to read an empirical study you bring with you a set of
standard expectations about the logic of research (as well as a developed, discipline-
directed set of critical reading skills honed through training in your field). You are
expecting to encounter independent and dependent variables (whose operationalization
can be critically assessed); you are expecting to see causal reasoning and perhaps a causal
model (the internal logic of which you know how to assess); you are expecting to find
statistical analysis—be it a regression equation, ANOVA or some other technique (you
might even turn immediately to the tables, before reading any of the text, in order to see
what sort of analysis is involved); and you are trained to assess the appropriateness of the
technique as a function of the measurement level of the data, to know whether underlying
statistical assumptions are met, and so on. Over time, you have become quite practiced at
reading and critiquing research conducted within this gestalt, and your application of
2
Research gestalts divide and cross disciplines in all of the social sciences (though the
proportions vary dramatically across disciplines). One consequence is that, for example,
a quantitatively trained sociologist may share more with a mathematician interested in
applied statistics than with his feminist departmental colleague who, with her collaborator
in cultural studies, examines the political meanings of rap music.


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