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Research > Research Basics pt. 2 > Flashcards

Research Basics pt. 2 Flashcards

1
Q

Parameter

A

-descriptive value for a population

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2
Q

Statistic

A

-descriptive value for a sample

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3
Q

Mean

A

-average
-most commonly used
-only used with interval/ratio
-influence by outliers
-toward the tail oppositte of mode

-Mu u

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4
Q

Variance

A

-SD^2 or (distance from mean)^2/ n-1

-Sigma^2

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5
Q

Standard Deviation

A

-distance between score and mean
- Square root (distance from mean)^2/ n-1

-Sigma

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6
Q

Frequency Distribution

A

-organized picture of an entire set of scores

-histogram, smooth curve, stem and leaf

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7
Q

Smooth Curve

A

-shows tthat the exact frequency is not being shown
-want it to be symmetrical (normal curve, mean and median are equal)

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8
Q

Histogram

A

-shows all the frequencies of the distribution

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9
Q

Skew

A

-non symmetrical distribution
-named for tail

Positive: scores pile up at low values, tail point to high values

Negative: scores pile ip at high values with tail at low

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10
Q

Kurtosis

A

-peakedness of tthe distrubution

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11
Q

Leptokurtic

A

-skyscraper
-higher and thinner peak
-low variability
-easier to get significance

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12
Q

Platykurtic

A

-hill
-lower peak
-higher variability
-harder to get significance

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13
Q

Stem-And-Leaf Display

A

-each score devided into a stem (first digit) or leaf (last digit)

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14
Q

Mode

A

-most frequent
-used in all data
-located on one side near peak, other farthest from mean

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15
Q

Median

A

-middle
-used for ordinal, intterval, or ratio
-unnaffected by outliers
-can’t show sig dif
-between mean and mode

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16
Q

Variabiltiy

A

-how spread out the data is
-descriptive (how spread out) and inferential stats (how accurate to pop)
-meaured by range or SD

More variability: less significance (platykurdic)

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17
Q

Range

A

-total distance

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18
Q

SD in Normal Distribution

A

-70% of scores 1 SD of mean (35+/-)
-95% of scores 2 SD of mean
-99% of scores 3 SD of mean

standardized, mean is 0

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19
Q

Z Score

A

-where a score is located relative to other scores
-# of SD above or below mean
-descriptive (where in curve) and inferential stats (reference to population)

z= score-mean/SD

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20
Q

Inferential Statistics

A

-infer things about the population based on sample

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21
Q

Probability

A

-proportion under the curve
-z score creates % as body or tail

22
Q

Critial Limit Theorem

A

-30 sample with be closly related to real pop

23
Q

T-Test

A

-compare 2 groups
-used fo smaller samples
-flater curve than normal distibution
-1 tail only considers 0.05 in one tail= higher chance of significance
-2 tail considers 0.05 in both (0.025 in each)= lower chance of significance

24
Q

F-Distribution

A

-ANOVA
-more than 2 or factorial research design

25
Q

Chi-Square Distribution

A

-comparing proporttions of people in diff groups
-comparres observed frequencies to expected

26
Q

Standard Error of Mean (SEM)

A

-value that describes the diff between the sample mean and true pop mean
-always smaller than SD
-smaller=less sampling error

-sample SD/Square root (n)

27
Q

Point Estimate

A

-mean of sample, estimates pop
-boarder of box-plot

28
Q

Interval Estimate (CI)

A

-confidence interval
-range of sample that can include the real pop mean
-span of box plot

29
Q

Box-Whisker Diagram (boxplot)

A

-Whiskers: range of scores
-Box: median (line), upper and lower quatile (25 and 75%)

30
Q

Bar Graph

A

-nominal or ordinal
-similar to hisogram with space

31
Q

Error Bars Charts

A

-bar shoes mean score

Can show
-CI, SD, or Stardard error of mean

32
Q

Scatterplots

A

-correlation
-can be grouped (R is important)

33
Q

Parametric Statistics

A

Analyzes quantitative data
-t test, anova, regression
-must meet assumptions
-based on distributions so must be normalized

34
Q

Non-Parametric Statistics

A

Analyze qualitative data
-spearman, mann-whitney u (independent t test, takes mean rankings), friedman’s ANOVA, wilcoxson (independent t test, takes mean rankings)
-violates the assumptions or have nominal/ordinal data

35
Q

Linear Regression

A

-show relationships
-make predictions

36
Q

Parametric Assumptions of T-Test

A

I/R Data
Normality
Homogenity of Variance
Free of Extreme Outliers
Independence of Observations

37
Q

Normality

A

-concern with smaller studies <30
-check skewednwss (<or> 2 is a problem)
-check histograms</or>

Non-Parametric
-Shapiro-Wilk Test: >0.5 is not significant

38
Q

Homogeneity of Variance

A

Differences in variance should be equal

Non-Parametric:
-Levene’s test: want it to be not significant >0.5

39
Q

Free of Influential Outliers

A

Regression: cook’s distance (>1 is bad)

40
Q

Independence of Observations

A

-scores must not follow a pattern over time
-scores from one participant cant influence another persons scores

Non-pArametrics:
Durbin Watson

41
Q

Regression Assumptions

A

Linearity
Homoscedasticity
Outlier testing in regression

42
Q

Homoscedasticity

A

-relationship statistics
-seen in a scatterplot’s residual score
-variance must be the same at all levels
-how close are all points to the line
-r^2
-heteroscedasticity is opposite

43
Q

Linearity

A

-data points arranged in a linear pattern
-seen in a scatterplot

44
Q

Residual Score

A

-distance of score from regression line on y axis
-ouliers are large

45
Q

Standardized Residual

A

-distance from line in terms of SD
- negative= under the line
-positive = over line
-0= on line

46
Q

Solutions to Violated Assumptions

A

Trim the data
Windoring: substitute outlier with highest score
Transform the data: take the log of the data
Bootstrapping is SPSS:
Non parametric Data

47
Q

Critical Region

A

-in the tails
-outcomes unlikely caused by chance

48
Q

How To Increase Power

A

-increase effect size
-decrease variability
-increase sample size
-increase alpha
-use a 1 tail test

49
Q

Independent T-Test

A

-compares 2 means of independent data
-different groups
-1 IV and 1 DV
-Man Whitney U

50
Q

Repeated Measures T-Test (Dependent)

A

-compaires matched pairs
-same participant twice
-more likely to be significant
-wilcoxon signed ranks

-does not need HOV

51
Q

Bonferroni Correction

A

-limits alpha inflation when testing the same data set multiple times and makinf a type 1 error
- divides alpha by number of tests run

Research (6 decks)

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