Midterm Flashcards

Question

Math with levels of measurement

Answer 1

Interval: add and subtract Ratio: +, -, /, x

Answer 2

Nominal: mode Ordinal: median and mode Interval + Ratio: all three

Answer 3

* aggregating multiple items * underlying construct is continuous * Measurement instrument is reliable

Answer 4

Otherwise can't get an average Presents fewer analytic choices

Answer 5

50th percentile/middle score First step: order scores Next: locate middle ((n+1)/2)

Answer 6

Tie between 2 for most repeated score (mode) 2 distinct peaks in distribution shape

Answer 7

Tie between >2 for most repeated score (mode)

Answer 8

All in middle if perfectly normal

Answer 9

Display distribution of a single variable * Variable listed from highest to lowest on one side, frequency on other

Answer 10

Graphical display distribution * quantitative variables don't have gaps between bars unless the score has frequency of 0

Answer 11

negative skew

Answer 12

negative skew

Answer 13

Percentile rank Number of scores converted into %

Answer 14

Difference between individual score and the mean of distribution (x-m), divided by the standard deviation √((⅀(x-m)²)/n)

Answer 15

Cohen's d d = (M₁ - M₂) / SD Formally use pooled SD

Answer 16

The average spread of all data points about their group mean (not the overall mean)

Answer 17

Line graph if x-axis variable (IV) has small # of values Scatterplot if x-axis variable (IV) has large # of values

Answer 18

Linear: pts fit into single, relatively straight line; Pearson's r Nonlinear: pts fit into curved line

Answer 19

* For linear relationships * From -1.00 to +1.00 * Limitation: restriction of range - limited range in sample relative to pop 1. Turn scores into z scores (x and y variables seperately) 2. Multiple x and y z-scores together for each individual 3. Take mean of cross products

Answer 20

* Present and compare mean score of groups when IV is categorical * Error bars for variability (extend one standard error in each direction) * if difference between means is greater than 2 standard errors, there is statistical significance

Answer 21

STandard error

Answer 22

* When IV is continuous (e.g. time) or small # of IV lvls * Use when IV is quantitative * Error bars for standard error

Answer 23

* Correlations between quantitative variables when x-axis (IV) has large # of lvls * Add regression line

Answer 24

* Enter seperately, then combine using software * assess internal consistency of the measure using Cronbach's alpha or Cohen's k

Answer 25

Bra graph and cohen's d

Answer 26

Line graph or scatterplot (check for nonlinearity and restriction of range) Pearson's r

Answer 27

* No relationship in the pop., relationship in the sample reflects only sampling error * Occured by chance * No relationship = Cohen's d or Pearson's r is 0

Answer 28

Strength of relationship Size of sample

Answer 29

Sample or more extreme result would be unlikely if the H₀ were true Reject the null hypothesis Statistically significant

Answer 30

5% chance or less of a result at least as extreme as the sample result if the null hypothesis were true Even when the H₀ is true and alpha is .05, the H₀ will be mistakenly rejected 5% of the time Greater than 5% chance ==> retain the H₀ (fail to reject)

Answer 31

* Rejecting the H₀ when it is true (false positive) * p value tells probability of making a type I error * Cause: sampling error * Reduce the chance of making Type I error by setting alpha to something less than .05 BUT then this raises probability of making Type II error!

Answer 32

usually only moderate to strong positive relationships are published, leading to published effects showing a stronger relationship than is really in the population

Answer 33

various decisions in the research process to increase the chance of a stat. sig. result Set alpha before!!!

Answer 34

* retaining the H₀ when it is false (false negative) * Cause: relationship lacks adequate statistical power to detect relationship (e.g. sample is too small) * Reduce the chance of making Type II error by setting alpha to something more than .05 BUT then this raises probability of making Type I error!

Answer 35

* probability of rejecting the null hypothesis given the sample size and expected relationship strength (pearson's r) * Complement of the probability of committing a type II error * Power of .80 is adequate - means there's an 80% chance of rejecting the null hypothesis for the expected relationship strength * To increase statistical power, increase the strength of the relationship or increase the sample size *

Answer 36

1 - .59 = .41

Answer 37

1. Balance risks vs benefits 2. Act responsibly and with integrity (maintain trust + transperency) 3. Seek justice (fair treatment) 4. Respect right and dignity (autonomy + consent)

Answer 38

* CAN institute of health rsrch * Nat sci and Eng. Rsrch council of CAN * Soc sci & humanities Rsrch council of CAN

Answer 39

1. Exempt rsrch - nonsensitive, standard, public info * Maintain confidentiality * Once approved, exempt from regular, continuous review 2. Expedited Rsrch - no greater than minimal risk * Reviewed by 1 member of IRB, or appointed subcommittee 3. Greater than minimal risk - needs full IRB review

Answer 40

Standard measure of the distance between a single point in the data and the overall mean for that variable z-distribution: * ranges from negative infinity to positive infinity * has a mean of 0 * has a standard deviation of 1

Answer 41

* Standard distribution with a mean of 50, and a SD of 10 * without negative values * can use this to scale any variable (e.g. IQ)

Answer 42

Use to express z-scores more intuitively Refers to the proportion scoring less than a particular value Obtained from z-table

Answer 43

Values describing population distribution

Answer 44

Centre at mu Dispersion indicated by sigma

Answer 45

x-bar = centre of the distribution s = standard deviation/dispersion of the distribution

Answer 46

state of reality vs decision reached in inferential testing * Type I error * Type II error (beta) * Power

Answer 47

* Study’s ability to find a difference if there is one * Correct rejection of the H₀ * 1-beta (type II error) = power * ↑ Power by ... 1. ↑ n 2. ↑ critical alpha 3. ↑ effect size (∆)

Answer 48

1. Directional - upper/lower tailed (X... more/lower than Y) or two-tailed test (X different than Y) 2. Establish rejection regions * two-tailed tests: split the alpha value in half (0.025) so non-rejection region is 0.5 - 0.025 = 0.475 * single-tailed tests: rejection alpha value (0.05) on distribution graph 3. If sample falls within rejection region, reject the null hypothesis and conclude that the alt hypoth. is likely correct

Answer 49

* Only relevant to specific sample stats * Conditioned on the null hypothesis being true * the false positive rate associated with a p value of .05 is usually around 30%, but can be much higher * silent on the magnitude and range of an effect * Even the most miniscule effect can be statistically significant if the sample size is large enough

Answer 50

* Null hypothesis is rarely true * ST provides a binary decision (yes or no) and a direction of the effect * Mostly interested in the size of the effect * Statistical vs practical significance

Answer 51

* probability of results due to chance * Represents chance of making Type I error * smaller value = more "unusual" (e.g. sample is different than pop. that its being compared to) * Fail to reject H₀ if p > ⍺ * Reject H₀ if p < ⍺

Answer 52

Calculate expected power before conducting a study based on estimated n, critical ⍺, expected or minimum effect size (from related rsrch) Avoid post-hoc power analysis!!!!!!

Answer 53

Measure of the strength of a relationship Unrelated to n and statistical significance * Can be statistically significant but trivial effect * Could be statistically insignificant, but notable effects (increase n to gain significance) Cohen's d = mean difference / SD * d = 0.2, small effect * d = 0.5, medium effect * d = 0.8, large effect

Answer 54

Alpha is arbitrary number/threshold (e.g. finish line) P-value is the actual measurement (e.g. race time)

Answer 55

allows you to calculate exact percentages (conditional probability based on the occurrence of previous outcomes of similar circumstances)

Answer 56

Mulitple operational definitions for same construct allows for more general conclusion if multiple measures have consistent scores

Answer 57

All of them!

Answer 58

Ordinal, interval, ratio

Answer 59

interval and ratio

Answer 60

COnsistency over time For consturcts that are assumed to be consistent over time

Answer 61

Responses across the items on a multiple-item measure * if all items represent the underlying construct, then people's scores should be correlated which each other * assessed by collecting and analyzing data * Split-half correlation: split items into two sets, compute score for each set of items, examine relationship between the two sets using Cronbach's alpha

Answer 62

mean of all possible split-half correlations for a set of items Used to measure internal consistency

Answer 63

quantitative judgements

Answer 64

Categorical judgements

Answer 65

extent that measure covers the construct check measurement method against coceptual def

Answer 66

Criterion Validity: extent that peoples scores are correlated with other variables (criteria) that are expected to be correlated * Criterion: any varibale that is expected to be correlated with the construct in question * Concurrent validity: when the criterion is measured at the same time as the construct * Predicitive validity: when the criterion is measured at some point in the future (after the construct has been measured) * Convergent validity: other measures of same construct

Answer 67

Discriminant Validity: extent to which scores on a measure are NOT correlated with measures of variables that are conceptually distinct (e.g. self-esteem and mood) low correlations = evudence of conceptually distinct construct

Answer 68

Is the data precise and reliable, at the lowest possible cost (Efficiency)? Can the method be applied successfully to a wide range of phenomena (Generality)?

Answer 69

Parallax error (incorrectly sighting the measurement). * e.g. don't read the measurement right, copy it down wrong Calibration error (if the scale is not accurately drawn) * e.g. scale on a map is wrong Zero error (if the device doesn’t have a zero or isn’t correctly set to zero) Damage (if the device is damaged or faulty). * e.g. bent level

Answer 70

Gross errors: human mistakes in reading instruments and recording and calculating measurement results Systematic Errors * Instrumental: shortcoming, misuse, measurement accuracy * Environmental: external and envrmtl factors * Observational: inaccurate readings, conversion error Random Errors: disturbance about which we are unaware

Answer 71

Measurement error Lack of random sample Alpha value too liberal Investigator bias Improper use of one-tailed test

Answer 72

Measurement error Lack of sufficient power (n too small) Alpha value too conservative Treatment effect not properly applied

Answer 73

saying/doing socially appropriate thing

Answer 74

subtle cues in the measure that reveal how the researchers expect participants to behave

Answer 75

Confirmation bias * find what u look for Recording bias * recall * Rely on imperfect record (e.g. memory) * Availability heuristic * Primacy/recency effect Halo effect * extraneous variables affect measures * common in subjective appraisal of ind diffs Social desirability bias * impression management * participants more likely to report positive info to the experimenter

Answer 76

* Participant types (good, bad, faithful, apprehensive) * Pygmalion - self-fulfilling prophecy; use doubl-blind * Hawthorne - alter bhvr (pierce!) * Halo - short-term improvement bc of novelt of treatment * Placebo - natural improvement, generalized effect of 'being in treatment', reinteropretation of outcome measures * Biosocial experimenter cues - age, sex, attraction * Psychosocial experimenter cues - warmth, status, etc

Answer 77

* Standardize experimenter-participant interaction * Use blinding techniques * Use deception (active vs passive) * Convince participant that you can detect lying