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Spring 2018 Quantitative Research I: Fundamentals

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Nick Scott

on 1 February 2018

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Transcript of Spring 2018 Quantitative Research I: Fundamentals

Frequency Tables
Descriptive Tool
Comparing theory with the facts
Quantitative Research I: Fundamentals
Operationalization
: turning concepts into measurable variables (e.g. age into "years of age", or age into "10 year intervals"; e.g. inequality –> "income difference" or –> "income bifurcation")

Variables:
traits that can change values from case to case (e.g., sex, social class, ethnicity, immigration status). Independent (x) –> Dependent (y)

Frequency Tables
: summarize distribution of a variable, continuous or discrete, by reporting the number of times each score of a variable occurred

Rules for categories of frequency distribution:
1. Exhaustive
: there must be enough categories so that all observations fall into some category

2 Mutually exclusive
: the categories must be distinct so that an observation will fall into only one category
There are two main statistical applications:
Descriptive statistics
Inferential statistics

Descriptive statistics:
Summarize one variable (univariate); the relationship between two variables (bivariate); the relationship between three or more variables (multivariate)

For example: x1(values) + x2(social class) –> y(protest)

Inferential statistics:
Generalize, or infer, from a sample to a
population through random sampling (more on
this in Quantitative Research II)
Today's learning objectives:

explain how measurement works
construct a proper frequency table
Through SPSS
What is causality? Does it even exist?
For example: x (lefties) –> y (protest)?

Conditions for causation:
temporality (x comes before y)
association (distributions change together)
theoretical explanation
Quantitative Research
establish causality,
accurately measure concepts (validity)
generalize and replicate findings (reliability)
Interaction between theory and data analysis
Generalization based on
random sampling
Theory is on top of
the wheel for a reason:

deduction
Practice
Primary Objectives
Limitations:
social sciences are not natural sciences
measurement creates artificial sense of accuracy
quantitative research ignores everyday, lived experiences that transcend categories
does only one reality exist?
how would operationalize the concept of 'home'?
Census operationalization
Deductive
: one begins with a theory for something, then goes out into the world and tests it (can we generalize it to a population?)

Positivist
: Social scientists should use similar methods of inquiry as the natural sciences; knowledge must be measured and gathered empirically through the senses; scientific statements exclude normative judgement and subjective bias.

Objectivist
: Social phenomena have a preexisting reality independent of our perceptions; social reality is fixed; we have little control over it.

Methods
: Surveys, Experiments, Statistics
Quantitative Paradigm
Reliability
: consistency of measurement
stability over time
internal reliability
inter-observer consistency

Validity
: actually gauging concepts
face validity
convergent validity
construct validity
From these roots
But first... applying concepts while making persuasive arguments
concepts: mobilities, home, work, public space, the body, intelligence, etc.
concepts contain multiple dimensions (e.g. social, cognitive, emotional intelligences)
concepts are abstractions, in reality only messy approximations exist
good arguments recognize this messiness by using counter examples, avoiding sweeping statements, and drawing on as broad a base of evidence as possible (including secondary sources)
good use of source
good thesis
avoid sweeping + unfounded statements
good annotation, use of source
Excellent array of evidence
Include all the pieces
thesis + argument need works
Example One: Different Objects
Example Two: One object, different perspectives
cite year of sources
define jargon
excellent counter example
excellent annotation +narrative
good use of source/paraphrasing
A-
B+
importance of good writing
Full transcript