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CONCORDIA UNIVERSITY / OTHER / AHSC 380 / What is Nonequivalence?

What is Nonequivalence?

What is Nonequivalence?

Description

AHSC 380 QUANTITATIVE RESEARCH METHODS


What is Nonequivalence?



STUDY GUIDE

MIDTERM EXAM: Tuesday October 16th, 11:45am.

TOP THINGS TO KNOW FOR THE EXAM:

❏ Lit review purpose

❏ Induction/Deduction

❏ Qualitative vs. Quantitative

❏ Ethical requirement for research

❏ Different research approaches

❏ Conceptualization/Operalization

Deduction→

❏ Deducing something

❏ Taking from greater/general knowledge & deduct an answer

❏ Big to small

❏ Make predictions based on logical rules

Induction→

❏ Going from specific occurance to generalized rule

❏ Inducing a theory out of small things

❏ Small to big

Inductive Process → Specific experiences

Deductive Process→ General rule

Quantitative came 1st, Qualitative 2nd

Quantitative:

❏ Test hypothesis that the researcher begins with

❏ Concepts are in the form of distinct variables


What is Reductionism?



❏ Measures are systematically created before data collection and are standardized ❏ Theory is largely causal and is deductive

Qualitative:

❏ Capture and discover meaning once the researcher becomes immersed in the data

❏ Concepts are in the form of themes, motifs, generalizations, and taxonomies

❏ Measures are created in an ad hoc manner and are often specific to the individual setting or researcher

❏ Theory can be causal or noncausal and is often inductive If you want to learn more check out geog 103

FALLACIES:

Nonequivalence→ error of interpretation with data

Reductionism→ Boiling things down to a SINGLE cause

Spuriousness→ Sneaky/Mathematical relationship withholding important concepts (hidden variable) Don't forget about the age old question of etsu.d2l
Don't forget about the age old question of ingredients of photosynthesis

False Dilemma→ -2 options -pushing you to chose one or the other Affirming the Consequent→ If A leads to B, you can conclude the presence of A. (being wet example he gave in class)


What is Spuriousness?



Post Hoc→ There can be many explanations to an outcome

Elements of a Research Proposal:

•Literature review

–Argument explaining your theoretical approach, providing a rationale for specific hypotheses, and supporting the need for/significance of the study

•Methodology

–Argument supporting the research approach, generally including: research design, population & sampling, measurement processes, ethical considerations, anticipated analyses

Conceptualization & Operationalization Conceptualization:

● Conceptualization→ The process whereby we specify and refine what we mean when we use particular terms Operationalization: We also discuss several other topics like elitefol

● Operationalization→ The development of procedures that will result in empirical observations representing the concept

Types of Definitions

● Nominal→ Represent some consensus, or convention, about how a particular term is to be used

● Operational→ A working definition for the purposes of inquiry— specifies precisely how a concept will be measured

Concepts & Variables

● Conceptualization/Nominal definition relate to the CONCEPT

● Operationalization/measurement relate to the VARIABLE intended to measure the concept and the ATTRIBUTES that make it up

1. We derive broad conceptualizations of our variables by examining existing literature If you want to learn more check out ncs department
We also discuss several other topics like ccj 2020 fsu

2. We DECIDE upon an operational definition that is adequate/useful for our research purposes

3. We make decisions about how to best operationalize our variables (our working definition) for our research purposes

4. We are keenly aware that we wish to maximize reliability and validity 5. We know that we must provide support/justification for each and every decision—EXPLICITLY ANSWER THE UNASKED “WHY?”

Measurement Quality

● Reliability→ Will the same data be obtained each time in repeated observation? ● Validity→ Does the measure accurately represent the concept it is intended to measure?

Ensuring Reliability

❖ Existing measures→ Someone (hopefully) already did the work for us ❖ Test-retest→ Take the same measurement more than once

❖ Split-half→ Compare half the indicators with the other half

❖ Reliability→ Of workers Ensure that all data collectors & coders are doing the same thing

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