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Psychology 2B > Statistics > Flashcards

Statistics Flashcards

1
Q

Correlational research

A

non-experimental research to determine whether two variables are related to each other with little or no efforts to control extraneous variables

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2
Q

Third variable problem

A

An unmeasured (unintended) variable that could be influencing the relationship between the two variables being examined

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3
Q

characteristics of a relationship

A

the direction of the relationship and the strength of the relationship

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4
Q

positive correlation (the direction of the relationship)

A

two variables tend to move in the same direction

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5
Q

negative correlation (the direction of the relationship)

A

two variables tend to move in opposite directions

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6
Q

The strength of the relationship

A

the variance within each variable -> covariance

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7
Q

covariance

A

a measure of how he two variables vary/change together

for the formula:
1. for each participant subtract their value from the mean, for both variables, and multiply them together, giving one value for each participant
2. Sum all the values created in step 1 together
3. Divide by the number of pairs of observations minus 1

if the value is positive -> positive covariance/relationship

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8
Q

problems with co variance

A

it is very sensitive to the units of measurement of the variables and therefore its value does not imply the strength of the relationship

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9
Q

Correlation coefficient

A

Divide covariance by the product of the SD of both varibles

allows for standardised covariance

the numeral value ranges from -1.00 (perfect negative correlation) to +1.00 (perfect positive correlation) providing information about the relationship between two variables

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10
Q

Directional hypothesis

A

there is a relationship with the nature of the relationship specified
e.g. There is a (strong/moderate/weak) positive/negative relationship

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11
Q

non-directional hypothesis

A

there is a relationship without specification of the relationship, no prediction of the nature

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12
Q

Pearson correlation

A

Pearson’s product moment correlation is a correlation analysis parametric test

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13
Q

Assumptions of a Pearson correlation

A
  1. Levels of Measurement: Continuous data (interval or ratio)
  2. Related pairs: two data points from each observation/participant
  3. Linearity: the relationship is linear
  4. Normality: Residuals are normally distributed (Q-Q plot of residuals used to show)
  5. Homoscedasticity: variability (spread) of one variable remains constant across the range of another variable
  6. Absence of outliers: outliers are data points/values that differ from the majority of the data
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14
Q

Spearman correlation

A

Non-parametric alternative to the Pearson correlation

spearman coefficient:
step 1: rank the scores for each variable separately
step 2: calculate differences between ranked pairs
step 3: square the diffeences
step 4: sum the squared differences
step 5: compute the spearman rank order correlation coefficient

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15
Q

no tied observations

A

all observations are unique

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16
Q

tied observations

A

some values are the same

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17
Q

independent correlations

A

different group - do not affect the data from the other group

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18
Q

dependent correlations

A

shared variables between correlations

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19
Q

Regression

A

determine if one variable predicts/explains the other variable

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20
Q

Regression analysis

A

a statistical technique to investigate and model the relationship between variables

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21
Q

simple linear regession

A

one predictor variable predicting one outcome variable

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22
Q

multiple linear regression

A

two or more predictor variables predicting one outcome variable

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23
Q

Regression model (Line of best fit)

A

prediction of outcome changes based on value of predictor

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24
Q

Residual/Error

A

Actual value - Predicted value
helps measure how much error there is in predictions
represents difference between the actual values and the predicted values generated by our regression model

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25
positive residual
model underestimates actual value
26
negative residual
model overestimates actual value
27
Linear regression model
y = a + bx y: outcome variable a: intercept - the value of y when x = 0 b: slope - how much the y value changes when increases by 1 unit x: predictor value
28
simple linear regression equation
Y = b0 + b1X + error Y: predicted outcome based on the regression model b0: intercept, y when x = 0 b1: change in predicted y for each unit increase in x x: predictor variable (factor influencing y) error: differences between actual and predicted values (measure of residuals)
29
Slope
b1 = r x sy/sx sd of x and y
30
intercept
b0 = mean of y - b1 x mean of x
31
assumptions of simple linear regression
1. levels of measurement of the outcome variable- interval or ratio 2. levels of measurement of the predictor variable - interval/ratio or categorical with two levels 3. independence - each observation is independent of the others 4. non-zero variance - values of the predictor variable vary and are not all the same; they do not have a constant value across all observations 5. linearity - relationship between 2 variables must be linear 6. normality - residuals should be normally distributed 7. homoscedasticity - the v ariance of the residual is constant along the values of outcome variable
32
R-squared
measures the proportion of the variance in the outcome variable that is predictable from the predictor variable coefficient of determination is an indicator of goodness of fit (proportion of variability in the outcome variable that is explained by the predictor variable) R-squared = 1 -> perfect prediction shows the proportion of variability in the outcome variable that is explained by the predictor variable
33
F-value
explains statistically significant amount of variance in the outcome
34
f-test
whether r-squared is statistically significant
35
positive regression coefficient (b)
indicates predictor and outcome variable move in the same direction
36
negative regression coefficient (b)
indicates predictor and outcome variables move in opposite directions
37
adjusted r-squared
adjusts R-squared for the number of predictors in the model and provides a more accurate measure of the goodness of fit
38
effect size for simple linear regression
cohen's f-squared f-squared = r-squared adjusted / 1 -- R-squared adjusted interpretation: small 0.02 medium 0.15 large 0.35
39
Assumptions of Multiple linear regression
1. Levels of measurement of the outcome variable: interval/ration 2. levels of measurement of the predictor variable: continuous or categorical with two levels 3. indepndence of observations 4. non-zero variance 5. linearity: relationship between two variables must be linear 6. normality of residuals 7. homoscedasticity - variance of residuals is constant along the values or predictor variables 8. no multicollinearity - overlap between coeffecients
40
intercorrelation
multiple correlations stacked
41
collinearity/multicollinearity
a portion of the variation in the outcome can be explained by two predictors, phenomenon only occurs during multiple regression
42
capitalising on chance
conducting so many tests with .05 alpha level on the same data, resulting in the increased likelihood of Type 1 error
43
Type 1 error
False positive - incorrectly identifying an effect
44
Type 2 error
false negative - incorrectly identifying no effect
45
ANOVa
analysis of variance conduct statistical tests to compare several groups at once
46
One-way between -subjects ANOVA
only one independent variable in which each participant only appears under one level/condition manual steps 1. calculate the mean for each group 2. calculate the grand mean (add up all individual scores from all groups and then divide by total number of observations) 3. calculate within-groups variance 4. calculate between group variance
47
explanations for between group variation
treatment effects individual differences experimental error
48
explanations for in group variation
individual differences experimental error -> only due to random error
49
total variance
between group variance + within group variance
50
F-ratio
test statistic for ANOVA F = Between-group variance ---------------------- within group variance larger value indicates greater likelihood that observed differences among group means are not due to chance
51
assumptions of the one-way between subjects ANOVA
1. level of measurement: DV is interval/ratio 2. Independence of each observation 3. normality: residuals should be normally distributed 4. homogeneity of variance: between groups
52
post-hoc test
conducted to compare ever group in study against all other groups to identify significant differences between them tests: Turkey honestly singnificant difference Bonferroni
53
assumptions of a one-way repeated measures ANOVA
1. levels of measurement: DV is interval/ratio 2. normality of residuals 3. sphericity - the variances of the differences between all pairs of conditions are approximately equal
54
sphericity
variance of the differences between any two conditions must be the same as the variance of the differences between any other two conditions test with mauchly test
55
pairwise comparison
specifically compare two groups at a time to see if their means are significantly different
56
main effects (factorial ANOVA)
an effect of a single IV on the DV irrespective of any other IV
57
interaction effects (factorial ANOVA)
an effect of each V at the level of the other IV
58
assumptions of two-way between-subjects ANOVA
1. Levels of measurement: DV is continuous 2. Normality of residuals 3. Homogeineity of variance - check w Levene's test
59
factorial ANOVA
statistical method used to study the effects of two or more independent variables (factors)q
60
complete factorial design
all levels of each IV are paired with all levels of every other IV
61
incomplete factorial design
all levels of each IV are not paired with all levels of every other IV

Psychology 2B (5 decks)

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