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Chapter 4: Theories in Scientific Research (4 APPROACHES OF THEORIZING,…
Chapter 4: Theories in Scientific Research
THEORIES
WHAT IS A THEORY?
a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989).
WHAT DOES A THEORY DO?
Theories should explain why things happen, rather than just describe or predict.
THE
EXPLANATION
IN A GIVEN THEORY REQUIRES WHAT IS KNOWN AS CAUSATIONS. ESTABLISHING CAUSATION REQUIRES THREE CONDITIONS
(3)
rejection of alternative hypotheses (through testing)
(2)
temporal precedence
(1)
correlations between two constructs
EXPLANATIONS
can be idiographic or nomothetic
Idiographic explanations are those that explain a single situation or event in idiosyncratic detail.
nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. Note: less precise, less complete, and less detailed.
EXPLANATIONS DEFINED
BUILDING BLOCKS OF A THEORY
THERE ARE 4
BUILDING BLOCKS
OF A THEORY
Constructs
Variables
Propositions
Logic
ATTRIBUTES OF A GOOD THEORY
CRITERIA FOR EVALUATING THE "GOODNESS" OF A THEORY ARE AS FOLLOWS:
Falsifiability:
Falsifiability requires presence of rival explanations it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with!
Explanatory power
Ask yourself, how much does a given theory explain (or predict) reality?
Logical consistency
Ask yourself, are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other?
Parsimony
Parsimony examines how much of a phenomenon is explained with how few variables.
4 APPROACHES OF THEORIZING
The
SECOND
approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework.
The
THIRD
approach to theorizing is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organizational learning.
The
FIRST
approach is to build theories inductively based on observed patterns of events or behaviors.
The
FOURTH
approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorizing using a deductive approach.
Chapter 5:
Research Design
4 Key Attributes of a Research Design
External validity: refers to whether the observed associations can be generalized from the sample to the population (population validity), or to other people, organizations, contexts, or time (ecological validity).
Construct Validity: Examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organizational learning are difficult to define, much less measure.
Internal validity: Internal validity examines whether the observed change in a dependent variable is indeed caused by a corresponding change in hypothesized independent variable, and not by variables extraneous to the research context.
Improving Internal and External Validity
Controls are required to assure internal validity (causality) of research designs, and can be accomplished in four ways: (1) manipulation, (2) elimination, (3) inclusion, and (4) statistical control, and (5) randomization.
(2)
elimination
: The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status
(4)
statistical control:
In statistical control, extraneous variables are measured and used as covariates during the statistical testing process
(5)
randomization:
the randomization technique is aimed at canceling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature
(1)
manipulation
: In manipulation, the researcher manipulates the independent variables in one or more levels (called “treatments”), and compares the effects of the treatments against a control group where subjects do not receive the treatment
(3)
inclusion:
In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender
Statistical conclusion validity: examines the extent to which conclusions derived using a statistical procedure is valid.
Research design is a comprehensive plan for data collection in an empirical research project. It is a “blueprint” for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes:
(2) the instrument development process
(3) the sampling process
(1) the data collection process
Popular Research Designs
2 categories of Research Designs are as follows:
Positive
Positivist designs are meant for theory testing. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research
ADDITIONAL DESCRIPTIONS INCLUDE:
cross-sectional field surveys
is where independent and dependent variables are measured at the same point in time
Secondary data analysis
is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by country from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay
Field surveys
are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview
Case research
is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building).
Experimental studies
are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the “treatment group”) but not to another group (“control group”), and observing how the mean effects vary between subjects in these two groups.
**longitudinal field surveys is when dependent variables are measured at a later point in time than the independent variables.
Interpretive
interpretive designs are meant for theory building. interpretive designs include case research, phenomenology, and ethnography.