Lecture 4 (stats) Flashcards
(52 cards)
1
Q
What are the steps of the empirical cycle?
A
- observation: the idea for the hypothesis
- induction: hypothesis, general rule
- deduction: prediction and operationalization
- testing: test the hypothesis and compare data to prediction
- evaluation: interpret results in terms of hypothesis
2
Q
theory?
A
set of principles explaining a general phenomenon
3
Q
hypothesis?
A
- explanation for a phenomenon which is informed and based on a theory
- predictions are derived from the hypothesis
4
Q
falsification?
A
disproving a hypothesis/theory
5
Q
independent variables?
A
cause, manipulated variable, predictor variable
6
Q
dependent variable?
A
outcome
7
Q
categorical variables?
A
- contain categories
- binary variable: if two options are available
- nominal variable: used to denote categories without an order
- ordinal variable: used to denote categories with an order
8
Q
continous variables?
A
- gives score on a scale and can take on any value of the scale used
- interval variables: need equal distances between the individual values
- ratio variables: require meaningful ratios of values in addition to equal steps between values (i.e. rating 4 is twice as good as rating 2)
- truly continuous variables can take on any value on the scale
- discrete variables usually only take on certain values
9
Q
measurement error?
A
- difference between actual true score and measured score
- can be due to usage of different measurement methods
10
Q
validity?
A
- does an instrument measure what its suppose to measure
- criterion validity: does an instrument measure what it is supposed to as established by certain criteria
- concurrent criterion validity: checking data using the new instrument and criteria for validity
- predictive criterion validity: if data can be used to predict observations at a later point in time
11
Q
reliability?
A
- does an instrument give consistent values for interpretation
- test retest reliability
12
Q
correlational research methods?
A
- involves observing natural events
- longitudional or cross sectional
13
Q
experimental research methods?
A
- introduce and take away an effect to establish causality
- confounding variable: hidden third variable that might be causing the cause effect link
14
Q
testing different entities?
A
- between-groups design: comparing results of different groups
- between-subjects design: each subject experiences only one condition
- independent design: no participant overlap between groups
15
Q
Manipulating the independent variable with the same entities?
A
- within subject design: type of repeated measures design where participants experience every condition
- repeated measures design: can be within subject design or pre and post intervention repeated measurements
16
Q
variation?
A
- unsystematic variation: small differences in measurement across conditions regardless of manipulation
- systematic variation: differences in performance in conditions due to manipulation
- in independent designs variation can be due to manipulation or due to differences on characteristics of the entities
- Randomization helps keep unsystematic variation to a minimum
17
Q
Systematic variation in repeated-measures designs?
A
- practice effects: different performance because of familiarity
- boredom effects: different performance because of boredom
- random assignment of the order of conditions helps eliminate this
18
Q
skeweness?
A
- lack of symmetry
- positively skewed: tail points to positive end and vice versa
19
Q
kurtosis?
A
- pointyness
- degree to which scores cluster at the ends of the distribution
- leptokurtic: positive kurtosis, lots of scores in the tails
- platykurtic: negative kurtosis, barely any scores in the tails
20
Q
frequency distribution?
A
plots how often data occur
21
Q
normal distribution?
A
- has a bell shape curve and is symmetrical
- kurtosis and skew are 0
22
Q
the mode?
A
- most frequent score
- graphs can be bimodal or multimodal if they have multiple modes
23
Q
the median?
A
- middle score of all scores when they are ordered according to magnitude
- when the data contains an even number of scores, the median is the average of the middle two values
- is unaffected by skew and extreme scores
24
Q
the mean?
A
- measure of central tendency, average
- Can be influenced by extreme scores
- Uses every score in the sample and is stable in different samples
25
range of scores?
- dispersion, subtract lowest from largest score
- Affected by extreme scores
- solution: interquartile range which can be calculated by subtracting the top half median from the bottom half median
interquartile range is not affected by extreme scores
26
deviance?
- can be calculated as deviance = X - mean of X
- for the total deviance you add up all deviance scores
27
sum of squarred errors?
- indicates the total dispersion/error from the mean
- calculated as SS = sum of squared deviances
28
standard deviation formula?
- s = the square root of (SS devided by N -1) with N being the total number of observations
- variance is s squared which represents the average dispersion
- (N - 1) represent the degrees of freedom, which signify the number of observations that are free to vary
29
probability density functions?
- common probability distributions that can be used to calculate probabilities
- the area under the curve reveals the probability of certain events happening
- normal distribution with sd = 1 and mean = 0 most often used as data sets can be converted into this distribution
- z score calculation: (X - mean of X) divided by s
30
reporting data?
- Scientific information about one's findings should be shared openly and in much detail
- APA guidelines should be checked for correct reporting
- Guidelines exist on what notation should be used to represent statistics
31
model fit?
how well a model represents the observed data
32
linear and non linear models?
- linear: use a straight line to represent data
- non linear: curve the line to represent the data, can sometimes be more fit to represent the data but are also more complex
33
how to predict the outcome?
- using the regression coefficient and a variable
- outcome of X = model + error of X
34
how to calculate deviance?
- deviance symbolizes error
- deviance = outcome of X - model of X
35
assessing the fit of a model?
- with the sum of squared deviances/erros (SS)
- for estimating a population parameter use variance formula
36
sampling distribution?
- uses a large number of hypothetical samples to estimate the population parameters
- can reveal how representative a sample is of the population
37
standard error?
- standard deviation of the sampling distribution
- reveals how widely the sample data are spread around the population parameter
- SE = standard deviation devided by squared root of N
38
central limit theory?
with larger samples, the sampling distribution will approximate a normal distribution with mean and sd close to the population parameters
39
confidence intervals?
- boundaries that are supposed to contain the true value of the population parameter for a percentage of the sample
- wider confidence intervals are worse representations of the true parameter
40
how to calculate confidence interval?
1. calculate z-score = (X - mean of X) divided by standard deviation
2. bounds calculated by = mean of X +/- (z score x standard error)
41
confidence intervals for smaller samples?
For n < 30 t-distributions can be used with the corresponding df = n - 1
42
overlapping confidence intervals?
- help narrow down the range of plausible scores
- significantly different estimates: if 2 CIs do not overlap they most likely come from different populations
43
p value?
p = 0.05 is used as a threshold for confidence because we want to reduce the probability of getting the results by chance alone
44
types of hypothesis?
- H0: null hypothesis, no effect
- H1: alternative hypothesis, effect present
- accepting one hypotheis means that data is very likely under that hypothesis
45
what are the steps for null hypothesis statistical testing (NHST)?
1. Establishing hypotheses
2. Establishing alpha, the significance level (usually 0.05)
3. Establishing power (sample size needed)
4. Calculate p-value and t test
5. Compare p to alpha
- if p below or equal to alpha we have reason to reject H0
46
one tailed test?
- aternative hypothesis says there is an effect in a specific direction (e.g., the mean is greater than or less than the specific value)
- smaller test statistic needed for significant result BUT only detects change in one direction
47
two tailed tests?
- alternative hypothesis is different than 0, there is an effect in either direction
- larger test statistic needed for significant result
48
type I error?
- rejecting the null when it is more likely to be true
- we believe there to be an effect but there is not one
- denotated by alpha and is the same as significance level which is equal to 1 minus confidence level
49
type II error?
- accepting the null when we should reject it
- we believe there to be no effect but there is one
- when type II error increases type I error decreases and vice versa
50
is it considered more harmful making a type I or a type II error?
- type I error since that means that science does not move foward
- context dependent (in medicine type II might be more harmful)
51
bonferroni correction?
- If multiple tests are conducted, the type I error rate has to be adjusted (control for familywise error rate)
- formula = type I error divided by k, with k being the number of comparisons
52
statistical power?
- probability that a test will find an effect if one exists
- depends on effect size, how large alpha (significance level) is, and sample size
