Dates of lectures
January 23
theory: A set of logically consistent ideas about the relationships between empirical phenomena (i.e., concepts) that permits those ideas to be tested using observations.
hypothesis: A conditional statement that is logically consistent with a theory and can be tested with observations.
concepts: Formally and logically developed ideas about classes of phenomena that a researcher seeks to study; the "building blocks" of theory.
variables: Concepts that have been logically developed to establish internal differences or attributes that can be empirically measured.
indicators: Observable phenomena that can be used to designate and distinguish measured differences within variables.
operationalization: The process of deciding how (i.e., creating a research design) to measure concepts and test hypotheses using emprical observations.
observation: The process of gathering empirical data to analyze toward the goal of testing hypotheses.
falsification: The attempt to disprove a testable proposition. In the scientific method, hypotheses are supported by failing to reject (that is, "falsifying") them based on empirical evidence.
deduction: Reasoning that moves from general principles (theory) to particular instances (empirical observations).
induction: Reasoning that moves from from particular instances (empirical observations) to general principles.
objectivity: A state of knowledge about the empirical world that is independent of the knower's perceptions and biases.
intersubjectivity: A state of knowledge about the empirical world that is shared and agreed upon by several knowers.
paradigm: A set of presuppositions on which scientific activity is built; the body of theories, ideas, models, test cases, and values shared by a scientific community; and the specific scientific accomplishments that influence future scientific activity.
explanation: Scientific analysis that is causal; that is, it accounts for (or explains) a change in one variable with changes in another variable or set of variables.
description: Scientific analysis that simply relates (or describes) how a variable changes, either by itself or when related to another variable.
understanding: Scientific analysis that is concerned with the process by which a change in one variable is associated with a change in another, either as a reasonable account of the intervening mechanisms or (in qualitative research) as a lived and meaningful experience for individuals and groups.
independent variable (x): The thing (phenomenon or event) that is hypothesized to bring about the effect of something else.
dependent variable (y): The thing (phenomenon or event) that the researcher is trying to explain.
positive relationship: A relationship between variables in which change in one variable brings about the same kind of change (i.e., "in the same direction") for another variable. When variables are positively related, an increase in one brings about an increase in the other, and a decrease in one brings about a decrease in the other.
negative relationship: A relationship between variables in which change in one variable brings about an inverse change (i.e., "in the opposite direction") for another variable. When variables are negatively related, an increase in one brings about a decrease in the other, and a decrease in one brings about an increase in the other.
linear relationship: A relationship between variables in which change in one variable brings about a constant rate of change for another variable.
independent relationship: A relationship between variables in which change in one variable has no effect on ("is independent of") change for another variable. In other words, there is no relationship between the variables.
intervening variable: A third variable that is simultaneously independent (to the original dependent variable) and dependent (to the original independent variable).
validity: A measurement principle in which the variables you observe actually demonstrate the concepts you seek to study. If your measurements are valid, then you really are measuring what you think you are measuring.
reliability: A measurement principle in which the measurement procedures you use can generate the same measurements if they were to be repeated at a different time, on a comparable sample, or (in qualitative research) by a different researcher.
error: A measure of the extent to which empirical observations cannot be described by the hypothesized model. There are two sources of error:
Random error occurs because of the innumerable factors (i.e., variables) and diversity of people that make up the real world that researchers study. Ultimately, researchers are unable to eradicate the occurence of random error.
Systematic error occurs because of a flaw in the operationalization of a research design. Researchers should try to reduce the occurence of random error as much as possible.
categorical variables: Variables that depict attributes or categories of a concept that cannot be reduced to a number or numerical scale; they vary in kind. There are two kinds of categorical variables:
Nominal variables do not vary according to a specific order. The categories of nominal variables are simply names.
Ordinal variables vary according to a specific order, but the degrees of separation between their ranks cannot be numerically specified.
numerical variables: Variables that depict attributes or categories of a concept that can be reduced to a number or numerical scale; they vary in degree. There are two kinds of numerical variables:
Interval variables vary according to a specific order; the degrees of separation between their ranks can be numerically specified, but no true zero point orders their measured variation.
Ratio variables vary according to a specific order; the degrees of separation between their ranks can be numerically specified, and a true zero point orders their measured variation.
to control for variables: A technique in explanatory analysis in which all possible determinants of a dependent variable are held constant (i.e., "controlled for"), save one: the suspected causal independent variable.
Commonly known as "average,"
a measure of central tendency calculated by adding up the
quantities of each unit in a distribution and then dividing
by the number of units.
A measure of central
tendency that represents the midpoint in a distribution of
ordered data.
A measure of central
tendency that represents the most frequent value in a
distribution.
A measure that indicates the
strength of the linear relationship between two numerical
variables. As r tends to +1, the two variables are
more strongly positively associated; as r tends to
-1, the two variables are more strongly negatively
associated.
An empirical standard of
confidence that the measured relationship between variables
is unlikely to have occurred by chance alone. Although there
are different formulae and standards for determining
statistical significance, most require that the probability
of the measured relationship occuring by chance be less than
5 percent to be declared "statistically
significant."
A table format for reporting
measurements of relationships between variables. The number
of columns correspond to the number of categorical
attributes in the independent variable (x); the number of
rows correspond to the number of categorical attributes in
the dependent variable (y); and each cell reports the
proportion of the sample for each type of relationship
between variables.
A relationships between
variables in which at least three criteria are empirically
established: 1. X and Y are
correlated. 2. X precedes Y in
time. 3. The X-Y
relationship is nonspurious; no other competing
variables account for the observed X-Y
relationship. 4. Carter (2001: 19)
offers a fourth and final criteria: The X-Y
relationship is intuitively pleasing; it fits with
our current understanding of how the world
functions.
A relationship between
independent and dependent variables in which the
hypothesized relationship is really caused by the influence
of a third variable that is independent and antecedent to
the other two.
In crosstabulations, spuriousness is revealed when the
direction and strength of the originally hypothesized
relationship (i.e., the difference of percentages in cells
of each row) diminishes or disappears to zero after a
third variable is controlled for with the use of partial
tables.
A hypothesis in which the
effect of more than one independent variable is studied.
In crosstabulations, a multivariate model is revealed by
changed but consistent relationships in the originally
hypothesized relationship after a third variable is
controlled for with the use of partial tables. (Hint:
compare the same cells across the two partial
tables.)
A third variable that
logically falls in a time sequence, and systematically
explains the hypothesized relationship, between the
independent and dependent variables.
In crosstabulations, an intervening variable is revealed
when the direction and strength of the originally
hypothesized relationship (i.e., the difference of
percentages in cells of each row) diminishes or disappears
to zero after a third variable is controlled for by
constructing partial tables. Note that this has the same
appearance as spuriousness; logical reasoning about the
sequence of the third variable is needed to determine
whether it is an intervening variable or hides a spurious
relationship.
The tendency for a third
variable to interact with the independent variable, thereby
altering the relationship between the independent and
dependent variables. Thus, the originally hypothesized
relationship between the independent and dependent variables
will vary under different conditions of the third
variable.
In crosstabulations, an interaction effect is revealed
when the originally hypothesized relationship maintains
itself in one partial table but diminishes or disappears to
zero in the other after a third variable is controlled
for.
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Quantitative social research in which one systematically asks many people the same questions, then records and analyzes their answers. |
A measurement instrument
that provides written instructions and questions which
respondents self-administer in order to provide data for
analysis.
A measurement instrument
that provides instructions and questions which the
researcher verbally administers to informants and records
their responses in order to gather data for
analysis.
Research in which one does
not gather data oneself but instead tests original
hypotheses using data previously gathered by someone
else.
Survey research questions in
which respondents choose from a fixed set of
answers.
Survey research questions
which respondents answer in their own words.
The kind of empirical case
or unit that a researcher observes, measures, and analyzes
in a study.
The percentage of
respondents who complete a questionnaire or interview.
Although researchers disagree about what constitutes an
acceptable response rate, most consider anything below 50%
to be poor and anything over 90% to be excellent.
A measure that sums or
combines many separate measures of a variable.
An index that measures the
intensity, direction, level, or potency of a variable
constructed along a continuum. Most scales measure ordinal
variables.
A form of multivariate
statistical analysis that permits the isolation of one
independent variable's effect while simultaneously
controlling for the effect of all others. A regression
equation expresses the relationship between two or more
variables algebraically, estimating the average change in a
dependent variable given a change in the independent
variable(s). In its simplest (linear) form, a regression
equation is usually written: a (alpha) is the constant
(a.k.a. intercept) b (beta) is the
regression coefficient (a.k.a. slope) X is the independent
variable e is the error
term
Y = a + b1X1 +
b2X2 + ... +
bkXk + e
where...Y is the dependent
variable
An operational construct
that represents a categorical variable as a
two-category numerical variable for statistical
analysis. E.g., for gender: female = 1, male = 0.
A measure of error that
indicates the total proportion of change in the dependent
variable explained by changes in all the independent
variables.
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A group of subjects that are selected for study in order to make generalizations about a broader population. |
The group of all subjects,
either known or unknown, from which a sample is
selected.
The individual units, often
individual persons, that comprise a sample.
Methods for drawing a sample
in which the probability of selecting population elements is
known. The researcher uses random sampling so that
the representativeness of the sample characteristics to the
(known) population characteristics can be statistically
calculated.
Methods for drawing a sample
in which the probability of selecting population elements is
not known. To satisfy concerns about the sample's
representativeness, the researcher must explicitly explain
how the sample represents the population from which it was
drawn.
The list from which the
elements of the population are selected. Sampling methods
are only as sound as the sampling frame operationalizes the
population.
A nonprobability sample in
which elements are drawn based on their availability to the
researcher. Also known as a convenience sample.
A nonprobability sample in
which the researcher asks the initial elements, usually
people, to refer other potential elements for inclusion in
the sample. The process is repeated until the sample grows
(i.e., "snowballs") to the size desired by the
researcher.
A nonprobability sample in
which the researcher selects elements for a specific
purpose, usually because of the unique characteristics of
the elements.
A probability sample that is
organized to capture known group differences among the
population. In the first stage, elements are sorted into
separate groups (called "strata") according to the selected
group characteristics, e.g., men and women, different racial
and ethnic groups. In the second stage, elements are
randomly sampled from within strata.
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Research that observes and analyzes the meanings, concepts, definitions, characteristics, metaphors, symbols, and descriptions of things. (By contrast, quantitative research counts and measures things.) |
Literally, "people" +
"writing." The description of a group, culture, or social
practice as its members understand it.
A method of research
involving participating and observing first hand in the
social behavior and groups you are studying.
Research about social groups
and behavior observed in their natural social environment.
Field research can entail any research methods, not just
participant observation.
Research that begins with
the observation stage. Typically, inductive researchers let
their research questions emerge from their initial
observations and analysis.
Scientific analysis in which
a typically unstudied phenomena is first examined in order
to discover and identify relevant features and
meanings.
An interview that does not
utilize a fixed schedule of questions, often because the
interviewer does not know in advance what all the relevant
questions would be.
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A setting for social behavior where the field researcher can enter without permission. |
A setting for social
behavior where the field researcher must obtain permission
from members before entering.
Research conducted without
the knowledge or consent of those being studied or by
misrepresenting the role of the researcher.
A problem of validity that
occurs when subjects are aware they are being studied and
alter their behavior from its "natural" patterns.
Writings in which a
researcher records his or her personal observations of the
field. Field notes constitute the sole data in participant
observation. They tend to include at least three kinds of
writing: (1) thick descriptions, (2) running hypotheses, and
(3) notes for further investigation.
A method of corroborating
observations by drawing together multiple types of evidence
gathered from different sources and/or different
methods.
A comparison group who is
not exposed to the influence of the independent variable
(e.g., the treatment in an experiment).
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A research method that seeks to isolate the effects of an independent variable on a dependent variable under strictly controlled conditions. |
The independent variable
that is administered to subjects in an
experiment.
A comparison group who is
exposed to the influence of the independent variable (e.g.,
the treatment in an experiment).
In an experiment, the
baseline measure of the dependent variable that is taken
before the treatment is given.
In an experiment, the
measure of the dependent variable that is taken
after the treatment is given and is compared to a
pretest.
The extent to which an
experiment has actually caused what it appears to
cause.
The extent to which an
experiment can be generalized to other settings, other
treatments, and other subjects.
In an experiment, the degree
to which the observed effect of the treatment is actually
due to the effect of measuring the dependent variable twice
(i.e., in pretest and posttest measures).
A kind of reactivity that
occurs when subjects demonstrate the expected effect of the
treatment not because of its actual influence, but because
they are aware of participating in an experiment.
The most fundamental
experimental design: the experimental and control group are
given the pretest, an experimental group is given the
treatment, and the experimental and control group are given
the posttest.
An experimental design with
two experimental and control group pairs, one of which is
not exposed to pretest measures.
An experimental design where
neither the experimental nor the control group is exposed to
pretest measures.
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An assessment of a study's ethics: are the risks to subjects and science greater than the benefits to science and society? |
A method for analyzing texts
that involves four stages. 1. variable
construction: deciding
which characteristics or themes of the texts
are to be analyzed and how they are to be
observed 2.
sampling: selecting the texts
to be analyzed 3.
observation: code (or
measure) each text for the characteristics or
themes 4.
analysis: aggregate the
measurements and make numerical descriptions of the
texts.
Elements of a text that are
already observable and readily measurable without (much)
interpretation by the content analyst.
Elements of a text that
require interpretation by the content analyst before they
can be observed and measured.
In content analysis, the
principle of confirming the accuracy of a coder's
measurements by checking (usually some sample of) them
against a second coder's measurements.
These definitions have been developed by Leonard Nevarez and in some cases have been informed by the following sources:
Babbie, Earl. 1992. The Practice of Social Research. 6th ed. Belmont, CA: Wadsworth PubBaker, Therese L. 1994. Doing Social Research. 2nd ed. New York: McGraw-Hill.
Berg, Bruce L. 2001. Qualitative Research Methods for the Social Sciences. 4th ed. Boston: Allyn and Bacon.
Carter, Gregg Lee. 2001. Doing Sociology with Student CHIP. 3rd ed. Boston: Allyn and Bacon.
Neuman, W. Lawrence. 2000. Social Research Methods. 4th ed. Boston: Allyn and Bacon.
Oliver, Melvin L. and Thomas M. Shapiro. 1997. Black Wealth/White Wealth. New York: Routledge.
Schutt, Russell K. 1996. Investigating the Social World. Thousand Oaks, CA: Pine Forge.