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

• one source = theory

• statement of why and how several or more concepts are related

• abstract, applies to multiple empirical cases

• complex or simple

• suggests predictions

• falsifiable

• 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

• units of analysis

• sample = subset of pop. actually observed

• sampling

• 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 of interest vs. population to which sample can generalize

• 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

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)

• 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:

• general rule: use highest scale of measurement possible (most information)

Summary

• research process

• asking research questions - sources

• developing hypotheses

• theory

• exploratory research

• collecting data

• populations, samples, sampling

• variables

• scales of measurement

Part 2:
 

Data collection methods

• observation – measuring instruments, participant, nonparticipant, unobtrusive

• interviewing - self-administered questionnaires, face-to-face/ telephone interviews, computer-assisted

• surveys

• 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

• cross-sectional vs. longitudinal

• cross-sectional = one point in time

• longitudinal = same sample followed over time

• time series

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

• cross-sectional vs. longitudinal