lexicon dictionary

8/14/2019 Lexicon Dictionary

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This is a compilation of different terms and their meanings which may be helpful in

doing a research course. Some were obtained by reading books by T. Kuhn, E.

Babbie, W. Neuman and Nachmias on research methods. Others were defined from

internet sources and class notes.

Epistemology The study of how we know what we know or the philosophy of

knowledge.

Paradigms A broad point of view on the way things are or the theory dominant in any

historical period

Logical positivism A philosophy that believes in using experiments to prove things or

only believe in things that can be observed and measured to prove that it is true.

Verstehen - A philosophy that is exploratory or in search of meaning. It is the emphatic

understanding of human behaviour.

Epistemological modes - The different ways of knowing what you know. It can bethrough scientific, tradition, religion, logics and convention etc.

Errors in ordinary human inquiry these are mistakes that are sometimes made from

inaccurate observation. For example, if the question was asked what colour shirt our

lecturer was wearing the first day of class, we may have to guess because most of our

daily observations were causal and semiconscious. However, if we deliberately made an

effort to observe from the first day of the class, would help reduce error. It can also be

made from overgeneralization. For example, out of two thousand persons at a gathering,

we interview only five and assumed that all the others were there for the same reason. It

can also result from selective observation, illogical reason and premature inquiry.

Clocks and clouds analogy In the debate between the two dominant paradigms

-logical positivism and verstehen, Almond likened clocks to logical positivism and clouds

to verstehen and shows that while the hard sciences can easily adhere to the scientific

method, just as clocksor timecan be shown in a structured manner, social science

isnt the same type of animal. The cloud-like nature of social phenomena is ever

changing, reshaping itself into different outlines with growing and shrinking depths and

mass.

Normal science (T.Kuhn) Routine verification of dominant paradigms.

Revolutionary science (Kuhn) An abrupt development of a rival paradigm that is

accepted only gradually by the scientific community.

Rival paradigm (Kuhn) - competing oralternative view to long-held, obvious-seemingassumptions

Dominant paradigm (Kuhn) - A single truth or world view thatdominates a field of

science at any one time e.g. Marxism a dominant theory at one point

Anomalies (Kuhn) things that can not be explained well that do not fit the pattern.


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Deduction / deductive reasoning the logical model in which specific expectations of

hypotheses are developed on the basis of general principles. For example, starting from

the general principle that all deans are meanies, you might anticipate that this one wont

let you change courses. This anticipation would be the result of deduction

Induction /inductive reasoning /grounded theory the logical model in which

general principles are developed from specific observations. Grounded theory is aninductive approach to the study of social life that attempts to generate a theory from the

constant comparing of unfolding observations. This is different from hypothesis testing,

in which theory is used to generate hypotheses to be tested through observations

Wallaces wheel of science analogy this is a cycle that starts with a theory, then a

hypothesis, then observation, then empirical generalization; this logical model is

deduction. At the other extreme, the reverse takes place in which the starting point is from

empirical generalization. This model is Induction

Quantitative analysis the numerical representation and manipulation of observations

for the purpose of describing and explaining the phenomena that those observationsreflect

Qualitative analysis the non-numerical examination and interpretation of observations,

for the purpose of discovering underlying meaning and patterns of relationships

Theory asystematic explanation for the observations that relate to a particular aspect of

life or a statement that organizes predicts and explains a general class of phenomena

Concept an abstraction representing an object, a property of an object, or a certain

phenomenon that scientists used to describe the empirical world

Conceptual or nominal definition (conceptualization) a definition that describes a

concept by using primitive and derived terms

Operational definition (operationalization) a set of procedures that bridges the

conceptual-theoretical and empirical-observational levels by describing the activities

needed to empirically observe a phenomenon empirically

Normative statement or proposition deals with values and addresses what should be

rather than what is, for example a statement saying, Jamaica should be a democratic

society, is an expression of a value judgment

Empirical statement or proposition means you can validate something as true by

giving evidence that it is true. For example a statement saying, Jamaica is a democratic

society, can be proven by using evidence. This statement is based upon facts

Variable something that varies,it has logical groupings of values/attributes, for

example, the variable gender is made up of the attributes male and female

Value / attribute characteristics or qualities that describe an object

Dependent variable - phenomenon thought to be caused, depend on, or to be a functionof another (independent variable). It is the variable the researcher wishes to explain


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Independent variable - measures of the phenomena that are thought to influence, affect,

or cause some other phenomenon

Control variable something you control, or that is held constant in an attempt to

explain or clarify the relationship between two other variables (dependent and

independent). It is used to test whether the observed relations between the two variablesare spurious

Continuous variable a variable whose attributes form a steady progression, such as age

or income. Thus, the ages of a group of people might include 30, 31, 32, 33, 34, and so

forth and could even be broken down into fractions of years

Discrete variable - a variable whose attributes are separate from one another, or

discontinuous as in the case of gender (male or female)

Hypothesis - A theory made practical that can be tested or a specific statement of

prediction. It describes in concrete (rather than theoretical) terms what you expect willhappen in your study.

Logic of hypothesis testing (5 steps)

first of allformulate hypothesis and stating what it is.

Secondly, collect data relevant to the hypothesis

thirdly, evaluate hypothesis in light of the data

fourthly, accept or reject hypothesis

finally, revise theory in light of new information.

Relationship joint occurrence or covariation between two or more variables

Direction tells whether the relationship is positive or negative

Magnitude / strength the extent to which variables covary positively or negatively

Positive relationship - an association whereby as the value of one variable increases, the

value of the other also increases or when one is present, the other is also present

Negative relationship an association whereby as the value of one variable increases,

the value of the other decreases or when one is present, the other is absent

Perfect relationship this is when two variables are completely correlated and the value

equals one (1)

Zero relationship there is no correlation between variables

Spurious relationship an apparent relation between the independent and dependent

variable that is found to be false because it can be explained by variables other than those

stated in the hypothesis

Measurement the assignment of numbers or other symbols to empirical properties

according to rules


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It can also be seen as a class of quantitative data measures often used in survey research

that captures the intensity, direction, level, or potency of a variable construct along a

continuum. Most are at the ordinal level of measurement. Examples of scale are Guttman

and Likert

Forced choice this is when a question is asked and the respondent is given two options

yes or no to choose one

Likert scale a scale often used in survey research in which people express attitudes or

other responses in terms of ordinal-level categories (for example, agree, disagree) that are

ranked along a continuum. It is a summated rating scale designed to assist in excluding

questionable items

Semantic differential a questionnaire format in which the respondent is asked to rate

something in terms of two opposite adjectives (for example, rate textbooks as boring or

exiting) using qualifiers such as very, somewhat, neither, to bridge the distance

between the two opposites.

Unit of analysis the who or what being studied for example, individual people. It isalso the most elementary part of the phenomenon to be studied; its character influences

subsequent research design, data collection, and data analysis decisions

Ecological fallacy the inappropriate generalization from more complex to simpler unit

of analysis

Reductionism this is a fault of some researcher; a strict limitation (reduction) of the

kinds of concepts to be considered relevant to the phenomenon under study

Cross-sectional study a study based on observations representing a single point in time

Longitudinal study a study design involving the collection of data at different points in

time

Determinism an approach to human agency and causality that assumes human actions

are largely caused by forces external to individuals that can be identified

Idiographic explanation an approach to explanation in which we seek to exhaust the

idiosyncratic causes of a particular condition or event

Nomothetic explanation an approach to explanation in which we seek to identify a fewcausal factors that generally impact a class of conditions or events

Criteria for causality the variables must be correlated (some actual relationship exist

between the two variables), the cause take place before the effect and the variables are

nonspurious(effect cannot be explained interms of some third variable)

Necessary cause represents a condition that must be present for the effect to follow (for

example, it is necessary to take college courses in order to get a degree)

Sufficient cause - represents a condition that, if it is present, guarantees the effect in

question. This is not to say that a sufficient cause is the only possible cause of a particulareffect (for example, skipping an exam would be sufficient cause for failing a course,


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though you could fail it other ways as well). Thus a cause can be sufficient but not

necessary

Tautology a statement that is true by virtue of its logical form alone

Population / universe the entire set of relevant units of analysis

Sample thisis a subset or a representation of a population

Representativeness that quality of a sample of having the same distribution of

characteristics as the population from which it was selected. By implication, descriptions

and explanations derived from an analysis of the sample may be assumed to represent

similar ones in the population. Representativeness is enhanced by probability sampling

and provides for generalizability and the use of inferential statistics

Random selection - a sampling method in which each element has an equal chance of

selection independent of any other event in the selection process

Random sample a sample in which the reseacher uses random number table or similarmathematical process so that each sampling element in the population will have an equal

probability of being selected

Probability sampling - sample units selected from the sampling frame according to some

probabilistic scheme

Nonprobability sampling a sampling method in which there is no way of specifying

the probability of each units inclusion in the sample

Sampling frame - the list of the sampling units that is used in the selection of the sample

Stratified sample this is when you group sampling frame elements according to

categories of one characteristic and sample from each group separately

Significance test(s) this indicates the probability that a relationship could have

occurred because of chance alone. It is also a class of statistical computations that

indicate the likelihood that the relationship observed between variables in a sample can be

attributed to sampling error only

Level of significance the probability of rejecting a true null hypothesis; that is, the

possibility of making a type I error

Null hypothesis a statement of no relationship between variables; the null hypothesis is

rejected when an observed statistic appears unlikely under the null hypothesis

Chi square used as a measure of association in descriptive statistics or in inferential

statistics. As a measure of association, chi square can be used for nominal and ordinal

data. It has an upper limit of infinity and a lower limit of zero, meaning no association

Data reduction this is using scientific analysis to reduce data from unmanageable

details to manageable summaries


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bivariate quantitative data for variables in the form of percentages across rows or down

columns for the categories of one variable

Measures of association - a single number that expresses the strength, and often the

direction, of a relationship. It condenses information about a bivariate relationship into a

single number

Proportionate reduction of error a method used to measure the magnitude of the

relations between two variables wherein one variable is used to predict the values of

another

Phi - this is a chi-square based measure of association that involves dividing the chi-

square statistic by the sample size and taking the square root of the result.

Cramers V - this is a measure of association based on chi-square

Lambda - a measure of association indicating the magnitude and direction of the

relationship between nominal variables. it reflects the proportional reduction in errorwhen values of the independent variable are used to predict values of the dependent

variable. A value of 1 means that the independent variable perfectly predicts the

dependent variable. A value of 0 means that the independent variable is no help in

predicting the dependent variable

Gamma A symmetric measure of association indicating the magnitude and direction of

the relationship between ordinal variables that ranges between negative 1 and 1. Values

close to an absolute value of 1 indicate a strong relationship between the two variables.

Values close to zero indicate little or no relationship.

Spearmans rho a statistics used to calculate the strength of the relationship between

two ordinal variables. It is the non-parametric alternative to Pearson Product Moment

correlation

Covariation a measure of how two variables both vary relative to one another

Pearsons r the Pearson product-moment correlation coefficient, a statistic that

specifies the magnitude and direction of relation between two interval-level variables, is

the most commonly used statistic in correlational analysis

R-square (coefficient of determination) this measures the percentage of the varianceexplained

Linear regression analysis a form of statistical analysis that seeks the equation for the

straight line that best describes the relationship between two ratio variables

Scattergram / Scatterplot is a graph on which the researcher plots each case or

observation, where each axis represents the value of one variable. A diagram to display

the statistical relationship between two variables based on plotting each cases values for

both of the variables

Regression line a line based on the least squares criterion that is the best fit to thepoints in a scatterplot


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Least squares criterion this is a formula that looks at the distance by which data is off

by

Correlation matrix this is a matrix of correlation or a method of presentation showing

the intercorrelations among several variables

Multiple regression a statistical technique that allows us to assess the relationshipbetween an interval variable and two or more interval, ordinal, or nominal variables. It

permits one to predict the value of a dependent variable from the composite effects of any

number of independent variables

Standardized regression coefficient (beta weight) this permits one to determine

which independent variables have the greatest impact

Regression assumptions these are assumptions made about variables for analysis so

that the results can be trustworthy and also to avoid a Type I or Type II error, or over- or

under-estimation of significance or effect size

ANOVA or Analysis of Variance is a method of analysis in which cases under study are

combined into groups representing an independent variable, and the extent to which the

groups differ from one another is analyzed in terms of some dependent variable. Then the

extent to which the groups differ is compared with the standard of random distribution.

There are two common forms: one-way analysis of variance and two-way analysis of

variance

Eta / eta squared - A measure of association that ranges from 0 to 1, with 0 indicating no

association between the row and column variables and values close to 1 indicating a high

degree of association. Eta is appropriate for a dependent variable measured on an interval

scale (e.g., income) and an independent variable with a limited number of categories (e.g.,

gender). Two eta values are computed: one treats the row variable as the interval variable;

the other treats the column variable as the interval variable.

Reliability analysis - allows you to study the properties of measurement scales and the

items that make them up. The Reliability Analysis procedure calculates a number of

commonly used measures of scale reliability and also provides information about the

relationships between individual items in the scale. Intra-class correlation coefficients can

be used to compute inter-rater reliability estimates.

Inter-item correlation - is a type of reliability analysis that gives the average or mean ofall the correlations

Alpha - this is a reliability model of internal consistency, based on the average inter-item

correlation.

Factor analysis - a statistical technique for classifying a large number of interrelated

variables into a limited number of dimensions or factors

Time-series analysis an analysis of changes in a variable (for example, crime rates)

over time

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