Quantitative Analysis, Research Paper Example

Introduction

This article looks at structural equation modeling and its place in the psychological canon.  Quantitative psychology, in contrast to other types of psychological articles studied in the past, have different main characteristics.  The main characteristics of a quantitative psychology article include the use of statistical analysis in order to prove a theory.  That can typically be done via a number of different statistical analytical techniques including t-tests, ANOVA, linear regression, and logistical regression. Once the main phenomenon is analyzed using quantitative techniques, the article draws a conclusion regarding the relationship between theory and the evidence.

Main Critique

This article does a sufficient job on several counts.  First, the article sufficiently introduces an important quantitative tool in assessing psychological studies:  structural equation modeling.  The authors introduce the background and methodology behind the tool with specific reference to what situations it may be used.  Structural modeling is typically used for specifying and estimating models of linear relationships among variables.  The point of a structural equation model is to include all relevant variables (both observable and unobservable variables) in an equation in order to understand their relationship to each other.  Structural equation models has caught on  as a way to model the complex relationships found in psychology.

The article also provides a robust methodology section looking at the substantial literature in the area of structural equation modeling.  In particular, the article looks at different articles and the types of research design they used.  Just as in qualitative research methods, where numerous different types of studies are used, the same is the case for quantitative methods.  Structural equation methods can be used in the following study designs: cross-sectional designs; longitudinal design; measurement studies; experimental studies; and other types of studies.

The article also addresses some of the weaknesses related to the method.  Quantitative methods, just as qualitative methods, have issues related to generalizability.  Sometimes researchers believe that although numerical data may seem more “objective” than qualitative data; however, a data set may only represent a small sub set of the population and is not generalizable to the larger population.   There is also a problem related to confirmation bias.  Due to the need for exact specification with structural equation models, researchers may become biased in the way they see the data and how it interacts with reality.

Overall, this article did  a fine job introducing the main tenets of structural equation modeling and how it is used in analyzing problems related to psychology.

References:

MacCallum, R.C. & Austin, J.T. (2000). Applications of structural equation modeling in psychological research.  Annual Review of Psychology. 51(1), 201-226.