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Research Methods > Lesson 5 (Stats) > Flashcards

Lesson 5 (Stats) Flashcards

1
Q

Nominal Scale

A

catagorical, distinct categories, least complex, fewest mathmatical operations

Ex: ice cream flavors, political parties, dietary restrictions

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2
Q

Ordinal Scale

A

assigned values to data based on rank or order, second least precise scale, not proportionally spaced
Ex: Army rankings, how depressed are you?

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3
Q

Interval Scale

A

involves the use of numbers wiht equal units of measuremnts ,

ex: fahrenheit, has no true zero temp or fixed beginning, SAT scores

cannot calculate ratios between scores

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4
Q

Ratio Scale

A

equal number of units with a true zero,
weight, or Kelvin temp scale
most precise of all the scales

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5
Q

Descriptive Statistics

A

data that is representattive of a sample of the population

typically refers to reporting means, standard deviations, falls on a normal curve

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6
Q

Inferential Statistics

A

generalizing data from a sample back to a population using probabilities and hypothesis testing to make inferences about the population from a sample

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7
Q

Assumption of Normality

A

extent to which a distribution of scores approximates the standard normal curve

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8
Q

Assumption of Linearity

A

extent to which 2 variables correlate in a linear fasion, some relationships may be more curved or circular, and violate this assumption, requiring a nonparametric statistic method ot analyze the data

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9
Q

Assumption of Independence

A

scores must be independent of each other, or influencing each other in any way, Ex: pre-test post test affects each other

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10
Q

Assumption of Homogeneity

A

data differs from each other similarly Ex: F Maximum test or Levine’ test

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11
Q

Central Tendency

A

math that is used in descriptive stats

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12
Q

Mean

A

the average of all numbers: Add all numbers and divide by the number of numbers

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13
Q

Mode

A

most frequent

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14
Q

Range

A

difference between the highest and lowest scores

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15
Q

Median

A

middle point, 50% of scores above and 50% below

Calculated by adding 1 to the total number of observations and dividing by 2, if a researcher has 11 scores, the median is the 6th score when arranged from high to low, if you have an even number, take the 2 middle scores and add them together and divide by 2

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16
Q

Bimodal distribution

A

set of data that has 2 modes, important to find out why this is happening if there is a distance between the modes

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17
Q

homogenous distribution of scores

A

a distribution laking variability

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18
Q

varience

A

how close scores are to the mean

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19
Q

standard deviation

A

square root of the variance

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20
Q

standard bell curve distribution

A

this illustrates the way the scores vary around the mean by standard units

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21
Q

left skew distribution

A

Or Negative skew, mode above median (the tail is on the left) mean is to the left of the median

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22
Q

right skew distribution

A

Or positive skew, mode to the left of median, mean is to the Right of the median, tail to the right

23
Q

quartile distribution

A

Instead of the standard deviation percentages, it goes by 25% sections

24
Q

stanine distribution

A

Break the bell curve into 9 different parts

25
Q

Z-Score

A

Same as Standard deviation, with a population raw data more than 30
Range from -3 to 3

26
Q

T-Score

A

Standardization from a population of less than 30 people
Range from 20-80

27
Q

Levels of Significance

A

The chance of an error occurring in the rejection of the null hypothesis, aka type 1 error

28
Q

Alpha Value

A

Aka P-Value: usually .5
Levels of significance which is the probability of accepting or rejecting a null hypothesis

29
Q

Null Hypothesis

A

Refers to a conclusion that there are no differences between compared groups

30
Q

Type I error

A

Or alpha error, occurs when after analyzing the data, there is a decision to reject the null hypothesis, when it is actually true (such as in the case of a confounding variable)

31
Q

Type II error

A

Or beta error, occurs when the researcher accepts the null hypothesis, although there is in actuality, a difference

32
Q

One tailed test

A

Directional experimental hypothesis, predicts the outcome

33
Q

Two tailed test

A

Non-directional experimental hypothesis, there will be some effect, open ended

34
Q

Parametric statistics

A

Data must be interval or ratio, and meets the 4 basic assumptions

35
Q

Non-parametric statistics

A

Used with nominal or ordinal types of data, or the basic assumptions are not met

36
Q

Chi-squared

A

Only one able to analyze nominal data
Parametric and non-parametric test (but not as strong as other parametric tests)

Can see what we expected to see vs what was observed, calculated by summing the total number of responses and divide by two

37
Q

Wilcoxon signed rank test

A

Statistical procedure used to compare differences btwn paired ranks or ordinal data

38
Q

Mann-Whitney test statistic

A

Statistical procedure used to compare difference btwn groups when data violates one plus assumption’s underlying inferential stats (normality, homogeneity etc)

Ordinal data, skewed data, matched pairs

39
Q

Kruskal-Wallis

A

Randomly sampled, test for 3 groups of IVs

40
Q

T-test

A

Used to compare the differences btwn groups and within

The difference btwn groups must be greater than the differences within the group

Two groups (control and intervention groups)

41
Q

ANOVA

A

Stands for Analysis of Variance (aka F test)

Used to compare the differences within each group, with differences btwn 2+ groups

Usu used when there is one IV and typically 3+ groups

DV must be in interval or ratio scale

42
Q

MANOVA

A

ANOVA with MULTIPLE analyses, EX: one group studies 20 min, one group studies an hour, + control group, analyze the data for math AND history classes = multiple analyses

43
Q

Two-way ANOVA

A

Use two IVs to study one DV (or outcome)

Ex: factors or IVs (3 levels of exercise, including one controlled group) plus two groups for calorie intake (one control, one low calorie) and you see how both factors affect weight loss

44
Q

MANCOVA

A

One IV, plus a control variable, looking at 2 DVs (outcomes)

45
Q

Positive correlation

A

Btwn 0 and +1
When this happens this other thing also happens (or mostly)
Relationship btwn this thing and that thing happens

46
Q

Negative correlation

A

A increases, B decreases (usually)

Values btwn 0 and -1

Closer to 0 is weaker correlation for positive or negative, closer to +1/-1 is strong

Below +/-0.3 = not significant

47
Q

Pearson - R

A

Most frequently used correlation stat

Only used with integral or ratio data

Only used for single correlations

48
Q

Spearman Rho

A

Ranked order correlation
Used to determine correlation btwn 2 variables when one of the variables is based on rank order data

Pearson r is much stronger

49
Q

Structural Equation Modeling

A

Or SEM, a type of regression (which is a thread to internal validity)

Help predict the range the score falls in on the retest

Predicts the very high and very low scores will move towards the mean

50
Q

Multiple regression

A

A statistical procedure used to calculate the relationship btwn a predicted variable and several predictor variables

Classic example: factors that predict success in college

51
Q

Factor analysis

A

Technique that is used to reduce a large number of variables into a fewer number of factors

52
Q

ANCOVA

A

An ANOVA, but with a covariate, a control variable

53
Q

Two way MANVOVA

A

Two sets of IVs, two DVs (outcomes)

Research Methods (2 decks)

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