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PSY-6217 Regression/Univariate Statistics > Concepts > Flashcards

Concepts Flashcards

1
Q

Fixed effects

A

ALL treatment conditions of interest are included in an experiment. Fixed effects cannot be generalized beyond treatment conditions

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2
Q

Random effects

A

Experiment contains only a random sample of possible treatment conditions; can be generalized beyond treatment conditions in the experiment (provided representative treatment conditions)

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3
Q

Repeated measures design

A

Different treatment conditions utilize the same organism (in human experiments, it means all participants are subjected to every level of an independent variable), and so the resulting data are related (i.e. within-subject design)

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4
Q

Between-subjects deisgn

A

Experimental design in which different treatment conditions utilize different organisms (in human experiments, it means participants are only subjected to one level of an independent variable), and so resulting data are independent

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5
Q

Order effects

A

Potential confounding effect in which observed differences are due to the order of treatments as opposed to the treatments themselves

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6
Q

t-statistic

A

Student’s t is a test statistic with a known probability distribution (the t-distribution). In regression, it is used to test whether a regression coefficient, b, is significantly different from zero; in experimental work, it is used to test whether differences between two means are significantly different from zero

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7
Q

Independent t-test

A

Test using the t-statistic that established whether two means collected from independent samples differ significantly

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8
Q

Dependent t-test

A

test using the t-statistic that establishes whether two means collected from the same sample (or related observations) differ significantly

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9
Q

Chi-square distribution

A

probability distribution of the sum of squares of several normally distributed variables. It is used to 1) test hypotheses about categorical data and 2) test the fit of models to the observed data

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10
Q

Chi-square test

A

Can apply to any test statistic having a chi-square distribution, it generally refers to a pearson’s chi-square test of the independence of 2 categorical variables. Essentially it tests whether 2 categorical variables forming a contingency table (table representing cross-classification of 2 or more categorical variables) are associated

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11
Q

Probability distribution

A

a curve describing an idealized frequency distribution of a particular variable from which it is possible to ascertain the probability with which specific values of that variable will occur. For categorical variables, it is a formula yielding the probability with which each category occurs

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12
Q

Sum of squares (sum of squared errors)

A

Estimate of the total variability (spread) of a set of data. First the deviance (the difference between observed value of a variable and the mean) for each score is calculated, and then this value is squared. The SS is the sum of these squared deviances

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13
Q

Test statistic

A

a statistic (a quantity that describes a sample) for which we know how frequently different values occur. The observed value of such a statistic is used to test hypotheses.

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14
Q

bi (regression coefficient)

A

unstandardized regression coefficient. Indicates the strength of the relationship between a given predictor, i, and an outcome in the units of measurement of the predictor. It is the change in the outcome associated with a unit change in the predictor.

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15
Q

Bi (regression coefficient)

A

The standardized regression coefficient. Indicates the strength of the relationship between a given predictor, i, and an outcome in a standardized form. It is the change in the outcome (in standard deviations) associated with one standard deviation change in the predictor

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16
Q

Hypothesis

A

a prediction about the state of the world

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17
Q

Null hypothesis

A

Reverse of alternative hypothesis that your prediction is wrong and the predicted effect doesn’t exist

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18
Q

Alternative hypothesis

A

Prediction that there will be an effect (i.e., that your experimental manipulation will have some effect or that certain variables will relate to each other)

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19
Q

Sampling distribution

A

the probability distribution of a statistic; the distribution of possible values of a given statistic that we could expect to get from a given population; a distribution of statistics obtained by selecting all possible samples of a specific size from a population

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20
Q

Residual

A

the difference between the value a model predicts and the value observed in the data on which the model is based. When the residual is calculated for each observation, the resulting collection is called the “residuals”

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21
Q

Residual sum of squares

A

a measure of the variability that cannot be explained by the model fitted to the data. it is the total squared deviance between the observations predicted by whatever model is fitted to the data

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22
Q

Linear model

A

Model that is based upon a straight line

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23
Q

Model (explained) sum of squares

A

A measure of the total amount of variability for which a model can account. It is the (Total Sum of Squares - Residual Sum of Squares)

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24
Q

Fit

A

The degree to which a statistical model (a formalization of relationships between variables in the form of mathematical equations) is an accurate representation of some observed data

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25
Q

Simple regression

A

a linear model in which one variable/outcome is predicted from a single predictor variable that takes the form of Yi = (b0 + b1X1) + Ei, where Y is the outcome variable, X is the predictor, b1 is the regression coefficient associated with the predictor and b0 is the value of the outcome when the predictor is zero

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26
Q

F-ratio

A

a test statistic with a known probability distribution (the F-distribution). It is the ratio of the average variability in the data that a given model can explain to the average variability unexplained by the same model. It is used to test the overall fit of the model in simple/multiple regression, and to test for overall differences between group means in experiments

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27
Q

ANOVA

A

Analysis of variance; procedure that uses the F-ratio to test the overall fit of a linear model. In experimental research, this linear model tends to be defined in terms of group means, and the resulting ANOVA is therefore a test of whether group means differ

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28
Q

ANCOVA

A

analysis of covariance; a statistical procedure that uses the F-ration to test the overall fit of a linear model, controlling for the effect that one or more covariates have on the outcome variable. In experimental research, this linear model tends to be defined in terms of group means, and the resulting ANOVA is therefore an overall test of whether group means differ after the variance in the outcome variable explained by any covariates has been removed

29
Q

Pairwise comparisons

A

Comparisons of pairs of means

30
Q

Planned comparisons/contrasts

A

A set of comparisons between group means that are constructed before any data are collected. They are theory-derived comparisons and are based on the idea of partitioning the variance created by the overall effect of group differences into gradually smaller portions of variance. These tests have more power than post hoc tests.

31
Q

Central limit theorem

A

States that when samples are large (~>30), the sampling distribution will take the shape of a normal distribution regardless of the shape of the population from which the sample was drawn. For small samples, the t-distribution better approximates the sampling distribution. We also know from this theorem the standard deviation of the sampling distribution (i.e., the standard error), will be equal to the standard deviation of the sample (s) divided by the square root of the sample size (N)

32
Q

Factorial ANOVA

A

ANOVA involving two or more independent variables or predictors

33
Q

Familywise error rate

A

The probability of making a type I error in any family of tests when the null hypothesis is true in each case. The ‘family of tests’ is loosely defined as the set of tests conducted on the same data set and addressing the same empirical question.

34
Q

Grand variance

A

the variance within an entire set of observations

35
Q

Mean square

A

a measure of average variability. For every sum of squares (which measure the total variability) it is possible to create mean squares by dividing by the number of things used to calculate the sum of squares (or some function of it)

36
Q

Omega squared

A

An effect size measure associated with ANOVA that is less baised than eta squared. It is a function of the model sum of squares and residual sum of squares, measuring the overall effect of the ANOVA (not very helpful)

37
Q

Partial eta squared (n^2)

A

the proportion of variance that a variable explains when excluding other variables in the analysis. Eta squared is the proportion of total variance explained by a variable, whereas partial eta squared is the proportion of variance that a variable explains that is not explained by other variables

38
Q

Mixed ANOVA

A

ANOVA for a mixed design

39
Q

Mixed design

A

Experimental design incorporating 2 or more predictors/IVs at least one of which has been manipulated using different participants/entities and at least one of which has been manipulated using the same participants/entities.

40
Q

Multicollinearity

A

Situation when 2 or more variables are very closely linearly related

41
Q

Parametric test

A

Test requiring data based on the normal distribution, which require 1) normally distributed data 2) homogeneity of variance (equal variances throughout data) 3) interval/ratio data 4) independence

42
Q

Standard error

A

Standard deviation of the sampling distribution of a statistic. For a given statistic (e.g., mean), it tells us how much variability there is in this statistic across samples from the same population. Large values, therefore, indicate that a statistic from a given sample may not be an accurate reflection of the population from which the sample came

43
Q

Standard error of differences

A

If we take several pairs of samples from a population & calculate their means, then we could calculate the difference between their means. If we plotted these differences between samples as a frequency distribution, we would have a sampling distribution of differences. The standard deviation of this sampling distribution is the standard error of differences. As such it is a measure of the variability of differences between sample means

44
Q

Homoscedascticity

A

An assumption in regression that the residuals at each level of the of the predictor variables have similar variances. In other words, at each point along any predictor variable, the spread of residuals should be constant

45
Q

Correlation coefficient

A

Measure of the strength of a relationship/association between 2 variables

46
Q

Independence

A

assumption that one data point does not influence another.

47
Q

Pearson’s correlation coefficient (Pearson’s product-moment correlation coefficient)

A

Standardized measure of the strength of a relationship between 2 variables from -1 to 1 (as one variable changes, the other changes in the direction of the absolute value of the amount)

48
Q

Standardization

A

Process of converting a variable into a standard unit of measurement. Typically used is standard deviation units (see z-scores); allows you to compare data when different units of measurement have been used

49
Q

Standardization

A

Process of converting a variable into a standard unit of measurement. Typically used is standard deviation units (see z-scores); allows you to compare data when different units of measurement have been used

50
Q

Confidence Interval

A

for a given statistic calculated for a sample of observations, the CI is a range of values around that statistic that are believed to contain, with a certain probability, the true value of the statistic (the parameter)

51
Q

Confidence Interval

A

for a given statistic calculated for a sample of observations, the CI is a range of values around that statistic that are believed to contain, with a certain probability, the true value of the statistic (the parameter)

52
Q

Cross-product deviations

A

Measure of the ‘total’ relationship between 2 variables. It = the deviation one one variable from its mean multiplies by the other variable’s deviation from its means

53
Q

Cross-product deviations

A

Measure of the ‘total’ relationship between 2 variables. It = the deviation one one variable from its mean multiplies by the other variable’s deviation from its means

54
Q

Homogeneity of variance

A

Assumption that the variance of one variable is stable at all levels of another variable

55
Q

Homogeneity of variance

A

Assumption that the variance of one variable is stable at all levels of another variable

56
Q

Independent ANOVA

A

ANOVA conducted on any design in which all IVs have been manipulated using different participants (i.e., all data comes from different entities)

57
Q

Independent ANOVA

A

ANOVA conducted on any design in which all IVs have been manipulated using different participants (i.e., all data comes from different entities)

58
Q

General format of statistical procedures

A

Outcomei = (model) + errori

59
Q

General format of statistical procedures

A

Outcomei = (model) + errori

60
Q

Assumptions of t-test

A

1) Normal sampling distribution 2) At least interval-level data 3) scores in different treatment conditions are independent (for independent t-test) 4) Homogeneity of variance (for independent t-test)

61
Q

Assumptions of t-test

A

1) Normal sampling distribution 2) At least interval-level data 3) scores in different treatment conditions are independent (for independent t-test) 4) Homogeneity of variance (for independent t-test)

62
Q

Assumptions of t-test

A

1) Normal sampling distribution 2) At least interval-level data 3) scores in different treatment conditions are independent (for independent t-test) 4) Homogeneity of variance (for independent t-test)

63
Q

p-value

A

The probability under a specified statistical model that a statistical summary of the data would be equal to or more extreme than its observed value; indicate how incompatible the data are with a specified statistical model

64
Q

p-value

A

The probability under a specified statistical model that a statistical summary of the data would be equal to or more extreme than its observed value; indicate how incompatible the data are with a specified statistical model; probability of the data given the null

65
Q

Normal distribution

A

a probability distribution of a random variable that is known to have certain properties (perfectly symmetrical)

66
Q

Mean square

A

Variance in ANOVA

67
Q

q

A

The studentized range statistic; Both NK & THSD are based on q; Range of means/ estimated standard error of the mean for a set of samples being compared used in post-hoc analyses

68
Q

Orthogonal polynomials

A

Create power terms (IV taken to successive powers)
Test for increasing numbers of bends by adding terms
Quit when adding a term does not increase variance accounted for;
Growth curve or trend over time. If time is our predictor variable, then any polynomial is tested by including a variable that is the predictor to the power of the order of the polynomial that we want to test; Do not interpret b weights for polynomials. They change if you subtract the mean from the raw data.

PSY-6217 Regression/Univariate Statistics (1 decks)

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