Research
Design & Scales of Measurement
Research process:
1) ask research question(s)
2) develop hypotheses
3) collect the data
4) analyze the data
5) evaluate the hypotheses
Asking research questions

Does gun control reduce violent
crime? If so, how much?

What types of people are
most likely to develop breast cancer?

What causes changes in the
size of salmon populations?

How many heroin addicts live
in Snohomish County?

How have academic achievement
scores in a school district changed over time?

sources  personal interest,
govt./nonprofit agency, interest group, business, sci. lit.
Developing hypotheses

specific predictions for
empirical research derived from theory or other sources

statement of why and how
several or more concepts are related

abstract, applies to multiple
empirical cases

exploratory research  no
specific hypotheses

accumulated empirical research
> theory
Collecting data
Populations, samples,
and sampling:

population = set of all individuals,
groups, organizations, objects, locations, time periods, or events of interest
for your study

e.g., pregnant women; Fortune
500 companies; adults in U.S.; Superfund sites

sample = subset of pop. actually
observed

probability  each element
or unit of a population has known chance of being selected

often involves random selection

nonprobability  not possible
to specify the likelihood an element or unit will be selected for the sample
Population 
Sample 
Sample
type 
pregnant
women 
every
other pregnant woman at 3 Tacoma clinics 

Fortune
500 
Fortune
500 cos. in WA 

adults
in U.S. 
random
sample of adults in households w/ telephones 

Superfund
sites 
65
largest sites in U.S. 


population of interest vs.
population to which sample can generalize
Variables and measurement

variable = characteristic
that can take on more than one value

values = numbers or symbols

e.g., blood pressure (mmHg),
industry (banking/retail/ manufacturing/etc.), Superfund site remediation
status (full, partial, none)
Scales/levels of measurement

determine which statistical
techniques are appropriate
nominal  values
indicate qualitatively different kinds

e.g., political party (Demo/Rep/Reform/etc.),
soil type (sandy/clay/loamy/etc.)

qualitative variable = nominal
variable
quantitative var. = ordinal
or interval var.
ordinal  values
indicate ordered categories

e.g., social class (upper/middle/lower),
trimester (1^{st}/2^{nd}/3^{rd}), degree of sunlight
(full sun, partial shade, full shade)
interval/ratio  degree
of distance/difference between values can be specified

categorical = nominal &
ordinal

measurement variable = interval/ratio

discrete vs. continuous
Warning: be careful
w/ numeric coding of nominal and ordinal scale data!
Scales of measurement
form a hierarchy:
interval
ordinal
nominal

general rule: use highest
scale of measurement possible (most information)
scale 
key
feature 
comparisons
betw. values 
interval 
distance 
different?
which larger? how much larger? 
ordinal 
order 
different?
which larger? 
nominal 
quality 
different? 
Summary

asking research questions
 sources

populations, samples, sampling
Part 2:
Data collection methods

observation – measuring instruments,
participant, nonparticipant, unobtrusive

interviewing  selfadministered
questionnaires, facetoface/ telephone interviews, computerassisted

records/documents/archives
More on measurement

error  variability in measurements
due to unreliability or imprecision

bias  systematic error 
affects all measurements in the same direction

measured value = actual value
+ chance error + bias

univariate  single variable

bivariate  relationships
between two variables

multivariate  relationships
among 3+ variables
hypotheses are predictions
about the value of a particular variable or relationships between variables
independent variable =
explanatory variable
dependent variable = response/outcome
variable
Research design

experimental vs. observational
designs

experimental  researcher
manipulates an independent variable to test causal relationship (does X
cause Y?)

random assignment to one
or another group

practical & ethical limitations
to experiments

observational (nonexperimental)
 researcher simply "observes" world

crosssectional vs. longitudinal

crosssectional = one point
in time

longitudinal = same sample
followed over time
Miscellaneous terms

cases = elements of/units
in sample

sample size = n = number
of cases
Summary

data collection methods 
interviewing, observation, archival records

experimental vs. observational

crosssectional vs. longitudinal