Concepts

 

 

 

scientific method:

The process of creating knowledge using empirical observations and logical analysis according to the conventions of the scientific community. The scientific method includes at least 5 stages:

1. Observe or have a question about some aspect of the world. 2. Propose a tentative statement, called a hypothesis, that is consistent with your observation or question. 3. Make predictions based on the logical implications of the hypothesis. 4. Test those predictions with further observations and analysis and modify the hypothesis in the light of your results. 5. Repeat steps 3 and 4 until there are no unsatisfactory discrepancies between hypothesis and observation.

 

 

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.

 

 

observation:

The process of gathering empirical data to analyze toward the goal of testing hypotheses.

 

 

empirical generalization:

The process of making claims based on empirical data observed in a particular context about the relationships between concepts in a broader set of contexts.

 

 

 

concepts:

Formally and logically developed ideas about classes of phenomena that a researcher seeks to study; the "building blocks" of theory.

 

 

variables:

Ideas that have been logically constructed to establish internal differences that can be empirically observed and measured; the empirical counterparts of concepts.

 

 

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.

 

 

 

indicators:

Observable phenomena that can be used to designate and distinguish measured differences within variables.

 

 

operationalization:

The process of (and decisions involved in) designing a study to measure variables and test hypotheses using emprical observations.

 

 

 

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.

 

 

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.

 

 

  

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.

 

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.

 

 

 

to control 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.

 

 

mean:

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.

 

 

median:

A measure of central tendency that represents the midpoint in a distribution of ordered data.

 

 

mode:

A measure of central tendency that represents the most frequent value in a distribution.

 

 

correlation coefficient (r):

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.

 

 

 

statistical significance:

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."

 

 

 

crosstabulation:

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.

 

 

 

causality:

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 (2003: 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.

 

 

 

spuriousness:

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 partial crosstab tables, 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.

 

 

multivariate model:

A hypothesis in which the effect of more than one independent variable is studied. In partial crosstab tables, 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.)

 

 

 

intervening variable:

A third variable that logically falls in a time sequence, and systematically explains the hypothesized relationship, between the independent and dependent variables. In partial crosstab tables, 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.

 

 

 

interaction effect:

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 partial crosstab tables, 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.

 

 

 

cross-sectional analysis:

An analysis of data gathered at one point in time.

 

 

trend analysis:

As part of a longitudinal (i.e., over time) research design, an analysis of comparable data gathered in different years from different subjects.

 

 

panel analysis :

As part of a longitudinal (i.e., over time) research design, an analysis of comparable data gathered in different years from the same subjects.

 

 

survey research:

Quantitative social research in which one systematically asks many people the same questions, then records and analyzes their answers.

 

 

 

unit of analysis:

The kind of empirical case or unit that a researcher observes, measures, and analyzes in a study.

 

 

secondary data analysis:

Research in which one does not gather original data oneself but uses data previously gathered by someone else.

 

 

regression analysis:

A form of multivariate statistical analysis that can isolate one independent variable's effect while simultaneously controlling for the effect of all other independent variables. 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: Y = a + b1X1 + b2X2 + ... + bkXk + e where... Y is the dependent variable 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

 

 

dummy variable:

An operational construct that represents a categorical variable (traditionally, a nominal variable) as a two-category numerical variable to make its statistical analysis possible. E.g., for gender: female = 1, male = 0.

 

 

 

adjusted R2:

A measure of error that indicates the total proportion of change in the dependent variable explained by changes in all the independent variables.

 

 

 

index:

A measure that sums or combines many separate measures of a variable.

 

 

 

scale:

An index that measures the intensity, direction, level, or potency of a variable constructed along a continuum. Most scales measure ordinal variables.

 

 

questionnaire:

A measurement instrument that provides written instructions and questions which respondents self-administer in order to provide data for analysis.

 

 

 

interview schedule:

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.

 

 

 

closed-ended questions:

Survey research questions in which respondents choose from a fixed set of answers.

 

 

open-ended questions:

Survey research questions which respondents answer in their own words.

 

 

  

response rate:

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.

 

 

  

sample:

A group of subjects that are selected for study in order to make generalizations about a broader population.

 

 

 

population:

The group of all subjects, either known or unknown, from which a sample is selected.

 

 

 

elements:

The individual units, often individual persons, that comprise a sample.

 

 

 

probability sampling methods:

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.

 

 

 

nonprobability sampling methods:

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.

 

 

 

sampling frame:

The list from which the elements of the population are selected. Sampling methods are only as sound as the sampling frame operationalizes the population.

 

 

 

availability sample:

A nonprobability sample in which elements are drawn based on their availability to the researcher. Also known as a convenience sample.

 

 

 

snowball 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.

 

 

 

purposive sample:

A nonprobability sample in which the researcher selects elements for a specific purpose, usually because of the unique characteristics of the elements.

 

 

stratified random sample:

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.

 

 

qualitative research:

Research that observes and analyzes the meanings, concepts, definitions, characteristics, metaphors, symbols, and descriptions of things. (By contrast, quantitative research counts and measures things.)

 

 

 

ethnography:

Literally, "people" + "writing." The description of a group, culture, or social practice as its members understand it.

 

 

 

participant observation:

A method of research involving participating and observing first hand in the social behavior and groups you are studying.

 

 

 

field research:

Research about social groups and behavior observed in their natural social environment. Field research can entail any research methods, not just participant observation.

 

 

 

inductive research:

Research that begins with the observation stage. Typically, inductive researchers let their research questions emerge from their initial observations and analysis.

 

 

 

exploration:

Scientific analysis in which a typically unstudied phenomena is first examined in order to discover and identify relevant features and meanings.

 

 

 

unstructured interview:

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.

 

 

open setting:

A setting for social behavior where the field researcher can enter without permission.

 

 

closed setting:

A setting for social behavior where the field researcher must obtain permission from members before entering.

 

 

 

covert research:

Research conducted without the knowledge or consent of those being studied or by misrepresenting the role of the researcher.

 

field notes:

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.

 

 

 

triangulation:

A method of corroborating observations by drawing together multiple types of evidence gathered from different sources and/or different methods.

 

 

 

experiment:

A research method that uses careful methods of comparison to isolate the effects of an independent variable on a dependent variable, typically under strictly controlled conditions.

The classical experiment has three features: 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.

 

 

treatment:

The independent variable that is administered to subjects in an experiment.

 

 

experimental group:

A comparison group who is exposed to the influence of the independent variable (e.g., the treatment in an experiment).

 

 

control group:

A comparison group who is not exposed to the influence of the independent variable (e.g., the treatment in an experiment).

 

 

field experiment:

An experiment that takes place in a setting where the IV occurs naturally.

 

pretest:

In an experiment, the baseline measure of the dependent variable that is taken before the treatment is given.

 

 

posttest:

In an experiment, the measure of the dependent variable that is taken after the treatment is given and is compared to a pretest.

 

 

 

internal validity:

The extent to which an experiment has actually caused what it appears to cause.

 

 

external validity:

The extent to which an experiment can be generalized to other settings, other treatments, and other subjects.

 

 

posttest distortion:

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).

 

 

 

reactivity:

A problem of validity that occurs when subjects are aware they are being studied and alter their behavior from its "natural" patterns.

 

 

 

Hawthorne effect:

A kind of reactivity that occurs when subjects demonstrate the expected effect of the treatment not because of its actual influence, but because they try to behave the way they think the researcher is looking for.

 

 

Solomon four-group design:

An experimental design with two experimental and control group pairs, one of which is not exposed to pretest measures.

 

 

 

posttest-only control group design:

An experimental design where neither the experimental nor the control group is exposed to pretest measures.