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**DQE > 767 Lexicon > Flashcards

767 Lexicon Flashcards

1
Q

Continuous variable

A

no breaks between data (time, weight, etc.)

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2
Q

Discrete variable

A

Breaks between data (children, cars, etc.)

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3
Q

Between subjects

A

independent samples design. Samples unrelated to one another (e.g. IQ of men and women of this class)

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4
Q

Within subjects

A

dependent samples / correlated samples. could be repeated measures. could also be paired data (e.g. couples, romantic, client-counselor, members on one team vs. members on the other).

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5
Q

Multivariate

A

Focus on multiple dependent variables - even if only one independent variable

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6
Q

Univariate

A

Focus on one dependent variable - singular dependent measure - simple Algebra

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7
Q

Data

A
  • research results from which inferences are drawn
  • Usually numerical
  • Can also be newspaper and magazine articles, biographical materials, diaries, and so on. Verbal materials
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8
Q

Analysis

A
  • categorizing, ordering, manipulating, and summarizing of data to obtain answers to research questions
  • reduce data to intelligible and interpretable form
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9
Q

Interpretation

A
  • based on analysis, makes inferences pertinent to the research, relations studied, and draws conclusions about the relations
  • relations within the research study
  • almost automatic with analysis
  • broader meaning of research data is sought
  • link to theory and other findings.
  • examine congruence or lack of congruence
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10
Q

Statistics

A
  • theory and method of analyzing quantitative data obtained from samples of observations in order to study and compare sources of variance of phenomena
  • make decisions to accept or reject the hypothesized relations between the phenomena
  • aid in drawing reliable inferences from empirical observations
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11
Q

Statistic

A

a measure calculated from a sample

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12
Q

Parameter

A

a population value

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13
Q

Multiple regression

A

analyzes the common and separate influences of two or more independent variables on a dependent variable

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14
Q

Canonical correlation

A
  • logical extension of multiple regression
  • adds more than one dependent variable to the multiple regression model
  • handles relations between sets of independent variables and sets of dependent variables
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15
Q

discriminant analysis

A

discriminate groups from one another on the basis of sets of measures

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16
Q

factor analysis

A
  • different in kind and purpose from other multivariate methods
  • help researcher discover and identify the unities or dimensions, called factors, behind many measures
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17
Q

Path analysis

A
  • graphic method of studying the presumed direct and indirect influences of independent variables on each other and on dependent variables
  • method of portraying and testing theories
  • requires researchers to make explicit the theoretical framework of research problems
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18
Q

Analysis of covariance structures

A
  • the ultimate approach to the analysis of complex data structures
  • the analysis of the varying together of variables that are in structure dictated by theory
  • Also called causal modeling and structural equation models
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19
Q

Index

A
  • an observable phenomenon that is substituted for a less-observable phenomenon (e.g. a thermometer gives readings of numbers that represent degrees of temperature)
  • number that is a composite of two or more numbers (means, medians, coefficients of correlations)
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19
Q

MAXMINCON

A
  • Maximize experimental relations and effects
  • Minimize error variance
  • Control extraneous event and variables
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20
Q

Theory

A
  • An abstraction, not reality
  • Best available explanation of a phenomenon at some point in time
  • Not static. You can never prove a theory. Only disprove it.
  • Set of propositions defined by interrelated constructs
  • Nature of interrelations
  • Are these relationships directional? Does one moderate another
  • Explain phenomenon.
  • If we can explain it, we can predict it.
  • Case conceptualizations are small theories. Micro theories
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21
Q

Science

A
  • Systematic, planned, controlled, empirical, critical investigation into phenomenon.
  • Guided by hypothesis and theories about relation between constructs.
  • Ideally, a self-correcting, evolutionary process.
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22
Q

Stocastic

A

Probabilistic

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23
Q

Deductive reasoning

A

You have known truths and laws and move from theories to data

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24
Q

Inductive reasoning

A

You do not have known truths and build theory from data

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25
Q

Level of discourse

A

A set that contains all the objects that enter discussion

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26
Q

Frequency distribution

A
  1. Primarily for descriptive purposes 2. Observed distributions can be compared to theoretical distributions (e.g. normal)
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27
Q

Profile analysis

A

Compare profiles of scores where all scores have been converted into same unit

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28
Q

Theory of errors

A

Given enough chance errors, they will distribute into a normal curve

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29
Q

Standard error of the mean

A

Standard deviation of an infinite number of means. Measure of chance or error in its effect on one measure of central tendency

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30
Q

Sub-set

A

A set that results from selecting sets from an original set (e.g., samples of a population)

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30
Q

Universal set (U)

A
  1. Set of all elements under discussion 2. Universe of discourse or level of discourse. 3. Population
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31
Q

Empty set (E)

A

The set with no numbers in it. Null set.

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32
Q

Cross-partition

A

New partitioning that arises from successively partitioning the same set U by forming all subsets of the form A ^ B

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33
Q

Regression line

A

Expresses the relation between X and Y including its direction and magnitude

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34
Q

Relation

A

A set of ordered pairs

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35
Q

Ordered pair

A

Two objects, or a set of two elements, in which there is a fixed order for the objects to appear

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36
Q

Function

A

A special kind of relation in which each element of the domain is paired with only one member of the range

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37
Q

Cartesian product

A

When each individual member of A is paired with each individual member of B and all possible pairs between the two sets are obtained

38
Q

Nominal

A

Name only - categories of an underlying construct

39
Q

Ordinal

A

Continuum of an underlying construct with categories built upon the continuum – ranks

39
Q

Interval

A

Categories are equidistant (equal interval) along the continuum of the underlying construct

40
Q

Ratio

A

There is an absolute, meaningful zero (complete absence of construct)

41
Q

Mode

A

Category that has highest frequency. Can be bi-modal or multimodal

42
Q

Median

A

Point at which half of scores are above and half are below (50 percentile)

43
Q

Percentile

A

Percentage of scores that fall below target score (involves ranking)

44
Q

Mean

A

Average Sum X/N

45
Q

Range

A

Difference between high and low score (high - low)

46
Q

Variance

A

Dispersion of data from the mean – sums of squares / n (or n-1)

47
Q

Standard deviation

A

Square root of variance. Take square root to put into original metric.

48
Q

Deviation scores

A

Scores represented as deviations from their mean

49
Q

Standard scores

A

Deviation scores divided by standard deviation

50
Q

Kurtosis

A

Peakedness or flatness of the distribution. 4th moment about the mean

51
Q

General Linear Model

A

Examines associations between variables. Can have multiple IVs and multiple DVs. Everything we do will fit into the GLM.

52
Q

Sums of squares

A

Sum of squared dispersions of the mean

53
Q

Cross-product of deviation scores

A

Allows us to compare variables. Another type of squared deviation score.

54
Q

Sums of cross-products

A

Tell us how variables relate – gives direction of association

55
Q

Covariance

A

SCP/N

56
Q

Pearson product moment

A

Correlation coefficient (r)

57
Q

Effect size

A

r squared (r^2)

58
Q

Sums of Squares Total (SST) is equivalent to

A

Sums of squares Y (SSY)

59
Q

Error =

A

1- r^2

60
Q

r^2 =

A

SSM/SST

61
Q

SSY =

A

SSM + SSE

62
Q

Randomization

A

Random assignment

63
Q

Formula of the General Linear Model

A

y = a + bx + e

64
Q

Our model in the GLM

A

a + bx

65
Q

In GLM, a =

A

Intercept - what is predicted value of y when x = 0?

66
Q

In GLM, b =

A

Slope. Rise/run. How much change in x based on change in y?

67
Q

In GLM, e =

A

Error. What we can’t explain.

68
Q

In GLM, a + bx =

A

Our model. What we can explain

69
Q

y hat y^

A

Predictive value of y

70
Q

Formula for predictive value of y

A

y-hat = a + bx

71
Q

y =

A

y-hat + e

72
Q

e =

A

y - y-hat

73
Q

Criterion of least squares (error)

A

Minimize SSe - Sums of Squares Error - only one possible line will give us this – our optimal line for our data

74
Q

Probability

A

Chance of something being yes vs. no

75
Q

b =

A

SCP/SSx

76
Q

Conditional probability

A

Given x (a certain condition), what’s the probability of y?

77
Q

Target population

A

Who you want to sample

78
Q

De facto population

A

Who sample really represents

79
Q

Z significance level

A

1.96z = 95%

80
Q

Poisson curve

A

Non-normal curve

81
Q

Probability density function

A

By formula, we know the exact shape of a distribution and know exact shape of curve

82
Q

Point prediction

A

b = some specific value that is not 0 (or between two values)

83
Q

Standard error

A

Estimate of chance fluctuation - measure against which outcomes of experiments are checked. Is the difference a real difference or merely a consequence of chance?

84
Q

Probability sample

A

Use some form of random sampling in one or more of its stages

85
Q

Nonprobability sample

A

Does not use random sampling.

86
Q

Quota sampling

A

Knowledge of the strata of the population– sex, race, religion, etc.– is used to select sample members that are representative, “typical” and suitable for certain research purposes

87
Q

Strata

A

Partitioning of the universe or population into two or more nonoverlapping (mutually exclusive groups)

88
Q

Purposive sampling

A

Characterized by use of judgment and a deliberate effort to obtain representative samples by including presumably typical areas or groups in the sample

89
Q

Accidental sampling

A

Taking available samples at hand

90
Q

Stratified sampling

A

Population is first divided into strata and then random samples are drawn from each strata. Capitalizes on between-strata differences.

91
Q

Cluster sampling

A

Partitioning the population into clusters then sampling the clusters randomly (streets, schools, etc.) Can have multi steps of cluster sampling

92
Q

Randomness

A

We cannot predict outcomes.

**DQE (27 decks)

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