Chapter 2 – Thinking Like a Researcher

This chapter is largely a vocabulary / terminology chapter. There is reasonable support for the correlation between language and thought processes, so I can see the link between the chapter heading and the content. I guess!

If you are really interested in the topic of the correlation between language and thought process, you can check out the Wikipedia Article on Linguistic Relativity. (One demonstration of this, which isn’t mentioned in the Wikipedia article, is the Anthony Burgess novel, A Clockwork Orange, with an extensive vocabulary appendix which you have to flip back and forth until you can read it without referencing. By the time you are able to do that, you may have adjusted some of your thought process to that of the anti-protagonist, Alex.)

Since most of the content is vocabulary / terminology, mostly I will be just cutting/pasting, so just assume everything came from the chapter.

Unit of Analysis – The object / target of the research / investigation / inquiry. The unit of analysis is important because it shapes what type of data you should collect for your study and who you collect it from.

[For example, for my research proposal, I would like to investigate whether people in the United States of America actually demonstrate characteristics of an Epistemological Crisis. I would be setting the “characteristics of an Epistemological Crisis”, and measuring something like the frequency (or some measurement) of this as exhibited by people of the United States. So, my Unit of Analysis is people of the United States (I think!)

Concepts – generalizable properties or characteristics associated with objects,
events, or people.

Construct – an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. Must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean.

Multi-dimensional construct – consists of multiple underlying concepts.

Operational definitions – Define constructs in terms of how they will be
empirically measured in Scientific Research.

Variable – a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables.

Nomological Network – overall network of relationships between a set of related constructs:

Independent variables – Variables that explain other variables.

Dependent Variables – those that are explained by other variables

Mediating Variables – those that are explained by independent variables while also explaining dependent variables

Moderating Variables  – those that influence the relationship between independent and dependent variables.

Control Variables – extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable.

Proposition – tentative and conjectural relationship between constructs that is stated in a declarative form.

Hypothesis – The empirical formulation of propositions, stated as relationships between variables. Scientific hypotheses should clearly specify independent and dependent
variables.

Theory – a set of systematically interrelated constructs and propositions intended to explain and predict a phenomenon or behavior of interest, within certain boundary conditions and assumptions. Theories represent a system of multiple constructs and propositions. A systemic collection of related theoretical propositions. A theory tries to explain a phenomenon.

Model – a representation of all or part of a system that is constructed to study that system. A model tries to represent a phenomenon. Descriptive models are frequently used for representing complex systems, for visualizing variables and relationships in such systems. Predictive models (e.g., a regression model) allow forecast of future events. Normative models are used to guide our activities along commonly accepted norms or practices.

Models may also be static if it represents the state of a system at one point in time, or dynamic, if it represents a system’s evolution over time.

The last section is weak / poorly written and/or I just am not getting it. Though I think some of the concepts may be important, I am copying / pasting from another source which I think is better. I have included the two terms the text goes over, and added the third term (abduction), because I think all three are related here and relevant.

Deductive reasoning: conclusion guaranteed
Deductive reasoning starts with the assertion of a general rule and proceeds from there to a guaranteed specific conclusion. Deductive reasoning moves from the general rule to the specific application: In deductive reasoning, if the original assertions are true, then the conclusion must also be true.

Inductive reasoning: conclusion merely likely
Inductive reasoning begins with observations that are specific and limited in scope, and proceeds to a generalized conclusion that is likely, but not certain, in light of accumulated evidence. You could say that inductive reasoning moves from the specific to the general. Much scientific research is carried out by the inductive method: gathering evidence, seeking patterns, and forming a hypothesis or theory to explain what is seen.

Conclusions reached by the inductive method are not logical necessities; no amount of inductive evidence guarantees the conclusion.

Abductive reasoning: taking your best shot
Abductive reasoning typically begins with an incomplete set of observations and proceeds to the likeliest possible explanation for the set. Abductive reasoning yields the kind of daily decision-making that does its best with the information at hand, which often is incomplete