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SSR1 > Lecture 4 (stats) > Flashcards

Lecture 4 (stats) Flashcards

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
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2
Q

theory?

A

set of principles explaining a general phenomenon

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3
Q

hypothesis?

A
  • explanation for a phenomenon which is informed and based on a theory
  • predictions are derived from the hypothesis
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4
Q

falsification?

A

disproving a hypothesis/theory

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5
Q

independent variables?

A

cause, manipulated variable, predictor variable

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6
Q

dependent variable?

A

outcome

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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
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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
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9
Q

measurement error?

A
  • difference between actual true score and measured score
  • can be due to usage of different measurement methods
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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
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11
Q

reliability?

A
  • does an instrument give consistent values for interpretation
  • test retest reliability
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12
Q

correlational research methods?

A
  • involves observing natural events
  • longitudional or cross sectional
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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
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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
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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
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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
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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
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18
Q

skeweness?

A
  • lack of symmetry
  • positively skewed: tail points to positive end and vice versa
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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
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20
Q

frequency distribution?

A

plots how often data occur

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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
Q

range of scores?

A
  • 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
Q

deviance?

A
  • can be calculated as deviance = X - mean of X
  • for the total deviance you add up all deviance scores
27
Q

sum of squarred errors?

A
  • indicates the total dispersion/error from the mean
  • calculated as SS = sum of squared deviances
28
Q

standard deviation formula?

A
  • 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
Q

probability density functions?

A
  • 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
Q

reporting data?

A
  • 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
Q

model fit?

A

how well a model represents the observed data

32
Q

linear and non linear models?

A
  • 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
Q

how to predict the outcome?

A
  • using the regression coefficient and a variable
  • outcome of X = model + error of X
34
Q

how to calculate deviance?

A
  • deviance symbolizes error
  • deviance = outcome of X - model of X
35
Q

assessing the fit of a model?

A
  • with the sum of squared deviances/erros (SS)
  • for estimating a population parameter use variance formula
36
Q

sampling distribution?

A
  • uses a large number of hypothetical samples to estimate the population parameters
  • can reveal how representative a sample is of the population
37
Q

standard error?

A
  • 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
Q

central limit theory?

A

with larger samples, the sampling distribution will approximate a normal distribution with mean and sd close to the population parameters

39
Q

confidence intervals?

A
  • 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
Q

how to calculate confidence interval?

A
  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
Q

confidence intervals for smaller samples?

A

For n < 30 t-distributions can be used with the corresponding df = n - 1

42
Q

overlapping confidence intervals?

A
  • 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
Q

p value?

A

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
Q

types of hypothesis?

A
  • H0: null hypothesis, no effect
  • H1: alternative hypothesis, effect present
  • accepting one hypotheis means that data is very likely under that hypothesis
45
Q

what are the steps for null hypothesis statistical testing (NHST)?

A
  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
Q

one tailed test?

A
  • 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
Q

two tailed tests?

A
  • alternative hypothesis is different than 0, there is an effect in either direction
  • larger test statistic needed for significant result
48
Q

type I error?

A
  • 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
Q

type II error?

A
  • 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
Q

is it considered more harmful making a type I or a type II error?

A
  • type I error since that means that science does not move foward
  • context dependent (in medicine type II might be more harmful)
51
Q

bonferroni correction?

A
  • 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
Q

statistical power?

A
  • probability that a test will find an effect if one exists
  • depends on effect size, how large alpha (significance level) is, and sample size

SSR1 (9 decks)

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