METHODS OF SOCIAL RESEARCH

Kenneth Bailey

Free Press, 1978

note: some of the equations and tables are not browser friendly....

**CHAPTER 1** - Stages of Social Research

1. Choosing the research problem and stating the hypothesis

2. Formulating the research design

3. Gathering the data

4. Coding the analyzing the data

5. Interpreting the results so as to test the hypothesis

**CHAPTER 2** - Factors Afflicting Problem Selection

1. Sociological paradigm operating from

2. Researcher's values - Goffman vs. value free so state

3. Degree of reactivity inherent in methodology used

4. Methodology used including degree of proof required

5. Scope of the study

6. How time is treated

Cross-sectional vs. latitudinal studies

**CHAPTER 3** - Constructing Social Explanations

Descriptive (to describe) vs. explanatory prediction study.
Def; concepts that can take on more than one value along a continuum are
called variables. If only one possible value it's a constant.

Theory construction

1. formulate basic concepts

2. write propositions (subtypes include: hypothesis, empirical generalization, axioms, postulates, and theorems)

(a) one variable - invariate

(b) two variables - bivariate

(c) three or more - multivariate (should try to make into
bivariate)

A hypothesis is a testable statement; an empirical generalization
is a relationship that represents an exercise in induction. One observes
a relationship and then generalizes to a broader category.

Axiomatic theory: Postulates, axioms, theorems -- it takes
the form of the deductive syllogism.

Bivariate relationships: Positive or negative, strength
of relationship, symmetrical, asymmetrical (independent and dependent variables),
linear or curvilinear, spurious or involves an intervening variable.

Generally the dependent variable is the one we wish to
explain and the independent variable is the hypothesized explanation. A
suppressor variable suppresses the relationship by being positively correlated
with one of the variables and negatively correlated with the other.

A ¬ C -->B<--A C--> B

Spurious Intervening

Blalock (68) = empirical phenomena must be directly amenable
to detection and monitoring by senses of observations, touch, hearing,
smell.

Three basic hypothesis testing process: classical, grounded,
operational.

most complete `³` Conceptual level x__ r _{1}
__y

`³` ¯ ¯

high possib. `³` r_{2} r_{3}

of measurement `³` Empirical level ¯
¯

error `³` x^{1}__ r _{1}^{1}
__y

x^{1} and y^{1} are called indicators
(measures, scales, indices) of the concept r_{2} and r_{3}
are generally called epistemic relationships and are usually untestable
and therefore assumed.

Grounded theory: that developed from data.

1) enter the field work phase without a hypothesis

2) describe what happens

3) formulate explanations on basis of observations

measurement `³` Conceptual level x__ r _{1}
__y

error reduced `³`

limits `³` r_{2} r_{3}

generality `³` Empirical level

x^{1}__ r _{1}^{1} __y

Operationalism: P.W. Bridgeman defined as we mean by any
concept nothing more than a set of operations.

measurement `³`

error absent `³` Conceptual level x r_{1}=r_{11}
y

by dfn `³` ô ô

can't abstract `³` Empirical level x^{1}
y^{1}

**CHAPTER 4 **- Measurement

__Nominal__ measurement is a classification system,
must be at least two categories and they must be distinct, mutually exclusive,
and exhaustive (a category for every case)

__Ordinal__ ranking

__Interval__ - how many units of difference between

__Ratio__ - a true zero point

Validity

__face validity__: determining if the instrument arrives
at the concept adequately

__criterion validity__: involves multiple measurement
of the same concept

__construct validity__: replace test sets and run through
construct to see if valid for both indices.

Conceptual level x_{1} x_{1} x_{1}
« x_{2}

¯ ÷ ø ¯ ¯

Empirical level x_{1}^{1} x_{1}^{1}
« x_{1}^{11} x_{1}^{1} « x_{2}^{1}

faceV. criterionV. or ö÷

x_{1}^{11}

constructV.

__Internal validity__ asks whether a difference exists
at all in any given comparison.

__External validity__ is the problem of interpreting
the difference, to generalize the results how far.

Assessing reliability (1) by use of alternate or parallel
forms of the same measure used simultaneously; or (2) repeated application
methods; or (3) split half method whereby researcher constructs a single
instrument containing twice as many items as needed.

**CHAPTER 5** - Survey Sampling

Objects of study called units of analysis {x, x, ...}
- population or universe -- where x is called a sampling element of sampling
unit a sampling frame

1968 Nixon % called by Harris (41%) and Gallop (43%) actual
(42.9%) samples. Size 2000 of 73 million.

Random sample - develop sampling frame of adequate size
to pull randomly - sampling units or elements.

Simple random sampling - sampling without replacement.
Means ignore subsequent number of one already drawn.

Appendix A - random tables explained pg. 76

If the researcher can find no evidence of biased ordering
there is little recourse but to assume random order.

Systematic sampling easier if one doesn't have a sampling
frame completed. Best if sampling frame is already randomized [considered
expensive]

Two assumptions for systematic sampling

1 that elements appear in random order with regard to characteristics of interest

2 sufficient number (K) over time (T)

Random interviewing street corner pick one of first x
and then every x+x or x+10, etc.

Stratified random sampling adv. smaller sampling size.
Mendenhall, Ott, Sheaffer (1971) defn: separating population elements into
non-overlapping groups, called strata, and then selecting a simple or random
sampling from each strata.

Dfn: Cluster sample (sometimes called area sampling),
is generally used when it is impossible or impractical to construct a sampling
frame in which the sampling units are the sampling elements themselves.

Disadvantage lack of control because multiple samples.

Nonprobability samples: ____vience, quota, dimensional (quota across universe), purposive (use of research and knowledge), snowball uses interview to find requisite characteristics then uses them to name others

Mean of sampling distribution µ mean for single sample

µ = S^{n} x_{i}¤N

Variance adds squares of deviances so as not to have signs
cancel each other

Variance = S^{ni=1}(x_{i}-x)^{2}¤N
= symbolized s^{2} for the population or universe and S^{2}
for the sample.

Therefore standard deviation s or S = Ö(x-)^{2}¤N

The greater the S or s the greater the sample size must
be. This is a sign of heterogeneity. If s is small, can perhaps reduce.

Standard error S.E. = (s¤ÖN)(Ö1-f¤1),
where f is the sampling fraction __# in sample __. Note that as f approaches
1, S.E. ® 0,

# in population

f approaches 1 as population ¥ Small f would mean
small N so large S.E.

**CHAPTER 6** - Questionnaire Construction

Longitudinal (time) studies: panel studies (same respondents), (topic) trend studies

Key word is relevance: avoid double barrelled, ambiguous, leading

closed-ended limited response

open-ended

Format - get quick, easy, closed questions over so as to gather data.

Mailed: cover letter, instructions, pretesting.

**CHAPTER 7** - Mailed Questionnaires: good & bad

Follow up

**CHAPTER 8** - Interview Studies

Flexibility, response rate, nonverbal behavior, control over environment, question order, greater complexity, question order, open-endedness.

Disadvantages: cost, time, less anonymity, bias.

Use prepared questions.

This is an attempt to record how personal composure and
job conditions are related. In particular I am studying individual reactions
to pressure situations. Because of the nature of my occupation - firefighting
- I am able to witness close encounters with stress. Being actively engaged
in my job responsibilities I am unable to record as my observations occur.
Also I am forced t keep my status as a researcher a secret from those I
work with. This is because of their dislike of observation and to prevent
contamination. For these reasons I have decided to do a field history record
as soon as possible after the incident.

An incident begins with a toned alarm. The tone is loud
and calls all hands to alert attention. As incidents present the stress
situation I have decided to do an incident-by-incident field history rather
than record daily.

**PART 3 - NONSURVEY DATA COLLECTION**

**CHAPTER 9** - Experiments

Control over causal variable (the experimental stimulus - often a movie). Can measure values before and after accurately.

Advantages:

1 Establishing causality: debatable if one can prove causality empirically

2 Control

3 Longitudinal analysis

Disadvantages:

1 Artificial environment

2 Experimentor effect

3 Lack of control soc and people make control difficult

4 Sample size

Experimenter must show closure.

Process: State hypothesis; measure dependent; introduce independent; remeasure dependent

Do this to separate groups leaving out independent and
show causality.

202 Solomon 3 group: only by adding a third control group, which as neither pretest nor test-stimulus effect, can we isolate extraneous effects. This is actually to experiment groups with one control group.

Factorial design: good for using variance. Cross checks variables involved.

Latin square design: presents as many independent variables
(experimental conditions) as there are subjects, but variables presented
in unique and different order for each subject.

Assignment of subjects:

Simple matching (find matching pairs and assign exp. & c)

Frequency dist: makes groups similar on average value of one variable.

Randomization.

Research outside of lab: Asch's study of conformity where
whole group set up on length of lines with one real subject.

Semi-experimental designs: studied in natural environment.

Ex post facto experiment: determining causal factors from
survey data.

Uncontrolled experiment.

Field experiments: the robber and the bystander: cases
of beer.

Validity & Reliability:

Valid to extent we can measure effect of independent on
dependent.

**CHAPTER 10** - Observation