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Research Methods > Exam 3 > Flashcards

Exam 3 Flashcards

1
Q

What is an appropriate measure to summarize ordinal data

A

Median

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2
Q

When a line graph is employed to represent the number of subjects who received each possible score on a variable, this graph is called a frequency ____\

A

Polygon

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3
Q

What is the difference between descriptive and inferential statistics?

A

Descriptive statistics summarize data

Inferential statistics determine the probability that results are due to chance

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4
Q

If you were to graph the results of tatste test between two different types of beverages, the type of beverage w hod appear on the

A

Horizontal axis

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5
Q

What would be an appropriate measure of centeral tendency to summarize interval data

A

Mean median and mode

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6
Q

In a _____ a separate and distinct _____is drawn to represent the number of people who received a possible score

A

Bar graph

Bar

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7
Q

The standard deviation would be an appropriate measure of variability only if the variable is measured on a _____ scale

A

Interval

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8
Q

In a survey examining the number of third vs 6th grade students who buy their lunch at school, the most appropriate description of the results would be to

A

Compare group percentages

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9
Q

A researcher wants to graph the results of a study that examined of food consumption affected preference for a movie. In This example, the preference for the movie should appear ___\

A

On the vertical axis

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10
Q

Example of histograms or frequency polygons

A

“clustered column” (Excel) or line graphs

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11
Q

Sampling error

A

When you only have a sample and not the entire population. This can result in skewed values

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12
Q

Statistic vs parameter

A

Statistic- characteristic of a small part of the population, i.e. sample.

parameter-fixed measure which describes the target population.

The statistic is a variable and known number which depend on the sample of the population while the parameter is a fixed and unknown numerical value.

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13
Q

Descriptive statistics

A

uses the data to provide descriptions of the population, either through nu

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14
Q

Inferential statistics

A

makes inferences and predictions about a population based on a sample of data taken from the population in question.

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15
Q

3 characteristics that completely describe a distribution:

A

Shape, central tendency & variability

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16
Q

Normal distribution shape

A

Symmetrical. Bell curve

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17
Q

Histogram/frequency polygon chart shape

A

Skewedness. Can be positive to negative

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18
Q

Frequency distribution

A

Shows number of instances in which a variable takes each of its possible values.

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19
Q

Central Tendency

A

– It is the score that indicates where on the scale the distribution is located
– It indicates the value of the variable around which most of the scores are found.
– A measure of central tendency is usually (but not always) near the center of the distribution.

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20
Q

Population

A

μ, N

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21
Q

Sample

A

n

_
X(or M)

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22
Q

Mode

A

Score that shows the most

Can be used on all for scales of measurement measurement (nominal, ordinal, interval, ratio)

Only one that can be used for nominal dependent variables

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23
Q

What is the only central tendency that can be used for nominal dependent variables

A

Mode

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24
Q

What can be used on all four scales of measurement

A

Mode

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25
Q

How do bar graphs differ from line graphs or histograms

A

Line graphs and histograms do not show frequency distribution

Histograms and line graphs are used for ratio and interval data

26
Q

Median

A

The median is the score below which half of the scores in the distribution fall; it is the 50th percentile

27
Q

“I make less money than average, but (almost) more money than most.
What am I?”

A

The median

28
Q

When can the median be used

A

ordinal, interval, ratio

29
Q

When is the median most useful

A

When describing skewed data

30
Q

In highly skewed data, the _____most accurately reflects the center of the scores.

A

Median

31
Q

Problems with the Median

A

it is based on counts of scores, not on the value of the scores.

32
Q

mean

A

Average score located at the exact mathematical center of a distribution

33
Q

Limitations of the Mean

A

Since the mean requires variables with equal intervals it can only be used on interval or ratio scales of measurement

extremely sensitive to extreme scores

34
Q

What central tendency method can only be used for interval or ratio data

A

The mean

35
Q

What are most inferential statistics based off of

A

The mean

36
Q

Variability

A

How different or spread out scores are in a distribution

37
Q

Ways to measure variability

A

Range

Sum/average of deviations

Sum/average of deviations squared

Variance (population & sample)

Standard deviation (population & sample)

38
Q

Range

A

The simplest, and least informative, measure of variability

Range = Highest Score – Lowest Score

39
Q

Problems with the range

A

Two different sets of numbers can have the same range, even though they are very different.

It does not take into account all of the information that is available in the entire set of scores.

40
Q

When is the Range used?

A

In descriptive manners.

Ex-describing age. The age ranged from 14-77

41
Q

Problems with Deviations from

the Mean. What to do about it

A

Average of the deviations always equals zero

What to do about it - The sum of squared deviations from the mean” or “Sum of Squares” (SS)

42
Q

Deviation from the mean

A

X-average

43
Q

Formulas for variance

A

“definitional” or “derivational”

“computational” or “raw score”

“calculational”(book)

44
Q

Problem with variance

A

Variance is unrealistically large, and is interpreted in squared units

– To get back to regular or “standard” units we take the square root after calculating the average squared deviation score (aka, variance)

45
Q

Standard Deviation

A

average distance from the mean

46
Q

Standard deviation is the same as…

A

Squared deviation

47
Q

Degrees of freedom

A

For samples, instead of (N), use (n-1) in the denominator

48
Q

What’s the issue with variance of a sample?

A

the variance of a sample is a little too small for (aka, underestimates) the actual population variance

49
Q

What is the z score of the top 5% or less

A

1.65

50
Q

What’s the z score for being in either the top or bottom 5%

A

1.96

51
Q

Standardization

A

the process of transforming a variable to one with a mean of 0 and a standard deviation of 1.

52
Q

why do we want to know z scores

A

we want to know where something where it is located in the distribution of scores.
(I.e., how far away a score is from the mean)

tells you the exact
location of the original X value within a
distribution.

53
Q

standardized

distributions and z scores

A

z scores help standardize distribution by allowing comparison of different distribution scores

54
Q

Binomial Distribution

A

These distributions tell us the probability for a specific number of “successes” to happen, given a probability of success and number of trials.

binomial distributions tell us the results of only two possible outcomes: success or failure.

An example of this is flipping a coin, which can only result in heads or tails.

55
Q

% of scores lie between 0 and 1 SDs

above the mean.

A

34.13%

56
Q

% of scores are between 1 and 2 SDs

above the mean.

A

13.59%

57
Q

% of scores are between 2 and 3 SDs

above the mean.

A

2.15%

58
Q

% of scores are more than 3 SDs above the

mean.

A

0.13%

59
Q

Alpha level

A

This is the probability level for significance.

60
Q

Sampling distribution

A

shows every possible result a statistic can take in every possible sample from a population and how often each result happens

61
Q

In a perfect relationship between two variables, r squared would be

A

Equal to 1.00

Research Methods (17 decks)

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