Lecture

January 11, 2010

Formulation of Theoretical Model and Research Problem (1)

THERE SHOULD BE THE POSSIBILITY OF SURPRISE IN SOCIAL RESEARCH. RESEARCH PROBLEM AS A PUZZLE

• Selecting Research Question

– A difference between advocacy research and scientific research

(Advocacy research refers to research that sifts through evidence to argue a predetermined position)

(Scientific research does not suppress contrary or inconvenient evidence)

Formulation of Theoretical Model and Research Problem (2)

• Researchable question– What to avoid?

Questions that imply answers dealing with different moral or aesthetic values

Questions that answering them involves unethical procedure

– Good questionsWhat proceeds why? Galileo’s maxim: description first, explanation

second

Formulation of Theoretical Model and Research Problem (3)

• Interesting question

“The heart of good work is a puzzle and an idea” (Abbott 2003, p. xi).

The no-surprise objection: “the answer is already well documented”, “we know answer before we do research” “the question is trivial”

The “so what” objection: “no relevance for social theory or for social life”

Formulation of Theoretical Model and Research Problem (4)

• Good research questions– Proposing new research

– Challenging prior research

– Extending prior research

To formulate good research problem and a theoretical model requires an extensive review of the literature.

Looking for a good review articles that provide (1) a theoretical grid or template, (2) an overview of key findings and unresolved issues, and (3) a description of the most influential studies.

Formulation of Theoretical Model and Research Problem (5)

• Choosing variables and specifying hypotheses

At minimum, any hypothesis involves two variables: an independent variable and a dependent variable.

- “You can’t explain a variable with a constant.” Maximizing variance to find the effect of a cause

- Substantive profiling: The use of telling comparisons

Preparation of Research Design (1)

A research design is a plan that shows, through a discussion of the model and data, how we expect to use our evidence to make inferences.

Model implies variables, units, and observations (values).

Data collection refers to observation, participant observation, intensive interviews, large-scale surveys, histories recoded from secondary data, ethnographies, randomized experiments, and other types.

Preparation of Research Design (2)

• How the data are collected?

Decisions: What data are available? What additional data will be needed?

Data collection is costly in terms of money and time.

- Sources of funding

- Timetable

We have to know how the data will be used.

Preparation of Research Design (3)

We have to know how the data will be used. Discussion of data analyses methods

Multi-method approaches

Measurement (1)

Types of sources:

• Verbal reports (self-report). Surveys

• Observation (firsthand, or through various devices)

• Archival records (statistical documents such as censuses, diaries, mass communications, and others)

Criteria of good measurement:

• Valid

• Reliable

• Exhaustive

• Mutually Exclusive

All involve measurement errors

Measurement (2)

• Observed reality = True reality + Error

Error = True reality - Observed reality

Minimizing errors through multi-indicator approach

Measurement (3)

• Missing data

Traditional approach

New approach:

- regression imputation

- random assignment

Sampling (1)

• .

Sampling (2)

• Target population --|> Frame population: Coverage error

• Frame population --|> Selected sample: Sampling error

• Selected sample --|> Collected sample: Non-response error

Coverage error and non-response error as the most serious errors in both qualitative and quantitative research

Sampling (3)

• Special issues:

- Samples for focus groups

- Samples for Internet studies

Data collection (1)

Politics of data collection

Data collection as a social process.

Sociology of data collection: Who needs what data for what purpose?

Data collection (2)

• Quality control of data collection

Analyses and interpretation (1)

• Statistics and substance in causal inferences

• Where the logic of qualitative and quantitative research is the same and – where it is different?

Analyses and interpretation (2)

• Special issues of causal inferences:

- endogeneity

- types of errors

– Type I (α): reject the null hypothesis when the null hypothesis is true, and

– Type II (β): accept the null hypothesis when the null hypothesis is false

Seven rules (1)

• Glenn Firebaugh, Seven Rules for Social Research. Princeton: Princeton University Press, 2008

• 1. THERE SHOULD BE THE POSSIBILITY OF SURPRISE IN SOCIAL RESEARCH. RESEARCH PROBLEM AS A PUZZLE

Seven rules (2)

• 2. LOOK FOR DIFFERENCES THAT MAKE A DIFFERENCE, AND REPORT THEM

• 3. BUILD REALITY CHECKS INTO YOUR RESEARCH

• 4. REPLICATE WHERE POSSIBLE

Seven rules (3)

• 5. COMPARE LIKE WITH LIKE

• 6. USE PANEL DATA TO STUDY INDIVIDUAL CHANGE AND REPEATED CROSS-SECTIONAL DATA TO STUDY SOCIAL CHANGE

• 7. LET METHOD BE THE SERVANT, NOT THE MASTER