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Psychology Statistics PSYCH203 > Exam 1 in-class notes > Flashcards

Exam 1 in-class notes Flashcards

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1
Q

Population description

A

The set of all the individuals of interest in a particular study

Vary in size; often quite large

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2
Q

Population notation

A

N

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3
Q

Sample definition

A

A set of individuals selected from a population.

Usually intended to represent the population in a research study.

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4
Q

Sample notation

A

n

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5
Q

Variable

A

Characteristic or condition that changes or has different values for different individuals

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6
Q

Parameter (or parameter estimate)

A

A value that describes a population

Derived from measurements of the individuals in the population

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7
Q

Statistic

A

A value that describes a sample.
Derived from measurements of the individuals in the sample.
Statistics are parameter estimates.We derive a statistic because we are trying to estimate a parameter.

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8
Q

Descriptive statistics goals

A

Summarize data
Organize data
Simplify data (means, tables, graphs, etc)

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9
Q

Inferential statistics goals

A

Study samples to make generalizations about the population.

Interpret experimental data.

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10
Q

Sampling error

A

the distance between a sample statistic and a population parameter.

Since the parameters are typically unknown we must estimate sampling error.

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11
Q

Sampling error indicators

A

1) Variability. Low variability = low sampling error

2) Sample size. Large sample = low sampling error

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12
Q

Constructs

A

Internal attributes or characteristics that cannot be directly observed.

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13
Q

Operational definition

A

Identifies the set of operations required to measure an external (observable) behavior.

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14
Q

Discrete variable

A

Has separate, indivisible categories.
No values can exist between 2 neighboring categories (i.e. cannot have 1.5 kids)

(these variable types refer to the underlying construct, not the operational definition)

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15
Q

Continuous variable

A

Has an infinite number of possible values between any two observed values.

(these variable types refer to the underlying construct, not the operational definition)

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16
Q

Nominal (named)

A

Labeled groups with no inherent quantitative relationship between each (e.g. diagnosed with disorder or not, restaurant options)

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17
Q

Ordinal (ordered or ranked)

A

Categorized observations by size or magnitude (i.e. S, M, L, XL shirt sizes, class ranking, placement in a race)

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18
Q

Interval

A

Ordered categories with equal size between categories of equal size.

Arbitrary zero point (IQ, Fahrenheit, Celsius)

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19
Q

Ratio

A

Ordered categories with equal size between categories of equal size

Non arbitrary zero point (height, weight, Kelvin)

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20
Q

Non-experimental (i.e. cross sectional) studies
Describe

IV
DV

A

examine associations between variables - no manipulation involved.

IV in these studies is called the Predictor variable.
DV is called the Outcome variable.

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21
Q

Statistical Notation

1) IV (predictor variable) is commonly referred to as __ variable
2) DV (outcome variable) is commonly referred to as the __ variable.

A

1) X

2) Y

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22
Q

N =

A

the number of scores within a population (the population size)

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23
Q

n =

A

the number of scores within a sample (the sample size)

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24
Q

Sigma ∑

A

defines what needs to be summed in a formula

25
Q

Order of Operations

When is ∑ done?

A

Summation is done AFTER operations in parenthesis, exponents and multiplication or division.

Summation is done BEFORE other addition or subtraction

26
Q

∑f = N

A

Meaning you sum the NUMBER of scores, not the value of those scores.

(add a pic to this slide)

27
Q 
Grouping Distributions - Rules of Thumb.
1.
2.
3.
4.
A
  1. Intervals should all be the same width.
  2. Make the lowest score of an interval a multiple of the width (e.g. 1.0-1.9, not 1.1-2.0)
  3. Use roughly 10 class intervals but use your best judgement (e.g. short report format > fewer; larger research report > more)
  4. Make the interval width a relatively simple number (e.g. 1, 2, 5, 10)
28
Q

Histograms rules of thumb

A
  1. X-axis lists intervals increasing from left to right, starting with 0 (unless negative scores)
  2. Y-axis lists frequencies increasing from bottom to top, starting with 0 (unless negative scores)
  3. Do not exclude intervals with a frequency of 0
  4. Width corresponds to score’s real limits (when ungrouped; class interval when grouped)
  5. Use for interval and ratio data
29
Q

When is it best to use a Histogram

A

for interval and ratio data

30
Q

Polygons

A
  1. Height of the bars in a histogram are replaced by a dot in polygon graphs. Dot height = frequency.
  2. Connect dots with continuous line
  3. Close the polygon with lines to the Y=0 point
  4. Can be adapted to grouped frequency distribution data
31
Q

When to use a bar graph

A

Nominal & ordinal data, to visualize frequency distributions

32
Q

Nominal & Ordinal Data - how to display

A
  1. Bar graph instead of histogram
  2. Signifies to readers the nature of the data at a glance
  3. Amount of space between bars does not matter, but they should be equal
33
Q

Nominal scales

A

REMEMBER:
Nominal (named) scales have no inherent order or value associated to the score. Data sets contain numbers, and usually not names so 1 = male, 2 = female, 3 = other; or 1 = Caucasian, 2 = African American, 3 = Asian/Pacific Islander, 4 = Latino/Hispanic, etc…

The value associated with the response is fairly meaningless and only serves to make distinctions between the groups.

34
Q

Ordinal scales

A

REMEMBER:
Ordinal (ordered or ranked) scales do provide information about inherent order, but do not indicate what the distances are between the ranks. E.g., in a 100m race, the ordinal scale will tell us who finished 1st, 2nd, 3rd, etc… but this scale does not tell us how spread out or clustered together the racers were (was the person in 1st place way ahead of the rest? Or did they barely win?)

35
Q

Interval & ratio scales

A

REMEMBER:
Interval & ratio scales contain information about the intervals or “distance” between values. Specifically, that they are equal. These scales provide the most information

36
Q

Nominal data doesn’t contain much information.

1) what does it have?
2) Best way to display?

A

1) Frequency is really all we’ve got, and from that we can get proportions.
2) Bar charts (better for emphasizing frequency), Pie charts (better for emphasizing proportions)

37
Q

Bar charts with nominal data are better (than pie charts) for…

A

emphasizing frequency

38
Q

Pie charts with nominal data are better (than bar graphs) for000

A

emphasizing proportions

39
Q

Histograms vs Bar charts

A

Bar charts imply distinct categories.

Histograms imply continuous variable measurements

40
Q

Visualizing frequency distributions - 2 types

A

Symmetrical

Skewed

41
Q

Symmetrical frequency distributions

A

Each side of the distribution is a mirror image of its counterpart

42
Q

Skewed frequency distributions

A

Scores tend to pile up on one side of the distribution or the other, which creates a “tail”

43
Q

Positive skew

A

Scores pile up on the LOW end of the measure (arrow on tail points towards + side)

44
Q

Negative skew

A

Scores pile up on the HIGH end of the measure (arrow on tail points towards - side)

45
Q

Central Tendency

A

A single score (value) that best represents the center of a distribution

46
Q

µ = ∑x÷N

A

Population Frequency Distribution

47
Q

M = ∑x÷n

A

Sample Frequency Distribution

48
Q

Population Frequency Distribution Equation (Mean)

A

µ = ∑x÷N

49
Q

Sample Frequency Distribution Equation (Mean)

A

M = ∑x÷n

50
Q

Mean

A

Sum of the scores divided by the number of scores in the data.

The amount each individual receives when the total is divided equally among all the individuals in the distribution

51
Q

Median

1) What it is
2) how to calculate

A

MID.
Midpoint of the scores in a distribution when they are listed in order from smallest to largest.

It divides the distribution into two groups of equal size.

Step 1. List scores from smallest to largest
Step 2. Pick the middle score (if an odd number of scores). For even number of scores - divide the middle two values - i.e. if middle scores are 4 and 5, median would be 4.5.

52
Q

Mode

A

Which score got the MOST.

The score or category that has the greatest frequency of any score in the frequency distribution.

  • Corresponds to an actual score in the distribution.
  • It is possible to have more than 1 mode
  • Distributions can also have none when they represent a uniform distribution.
53
Q

In a skewed distribution:

1) Mean
2) Median
3) Mode

A

1) gravitates toward the tail in a skewed distribution
2) gravitates toward the tail, but not as much as the mean
3) tends to be closer to the short tail

54
Q

If the mean - median > 0

TEST QUESTION

A

the distribution is positively skewed

55
Q

If the median - mean < 0

TEST QUESTION

A

the distribution is negatively skewed

56
Q

the distribution is positively skewed if the mean - median (greater than or less than) 0

TEST QUESTION

A

>

57
Q

the distribution is negatively skewed if the mean - median (greater than or less than) 0

TEST QUESTION

A
58
Q

Vector

A

?data set?

Psychology Statistics PSYCH203 (1 decks)

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