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Terms in this set (51)
Qualitative data
Data that is categorical (non-numerical)
e.g. gender, college education level, nationality, SSN, etc.
Quantitative data
Data are usually quantitative (numbers/numerical- can be mathematically measured)
e.g. ht, wt, bp, income level, etc.
Independent variable
The hypothesized cause of, or influence on, an outcome (IV effects DV)
Dependent variable
The outcome of interest, hypothesized to depend on, or be caused by, the independent variable
Discrete
Finite set of values between two points for this type of variable (countable)
e.g. # of students in a classroom; # of finger a person has; # of pts a doctor has, etc.
Continuous
Infinite set of values between two points (non-countable) (decimal numbers)
e.g. wt, ht, time
Measurement
Involves assigning numbers to qualities of people or objects to designate the quantity of the attribute, according to a set of rules
Nominal
Lowest level or measurement ranking- no mathematical purpose
Numbers are used as labels to name categories i.e. 0 = man, 1= woman
Ordinal
Level of measurement used for ranking
Does not indicated distance between values but conveys some info about the amount
Interval
Numbers are used to designate ordering on an attribute and conveys info about amount, used for quantitative
Distance between values are assumed to be equal
Ratio
Highest level of measurement- uses numbers to designate ordering, conveys info about amount, distances are equal
There is a real, rational 0
Averages can be computed
e.g. Medication doses
What type of measurement is used for qualitative data?
Nominal
Scale
A graduated range of values forming a standard system for measuring or grading something
Lower or higher level scales are usually preferred?
Higher
Sample
A small part or quantity intended to show what the whole is like.
Descriptive statistics
Describe and summarize data about the sample
Inferential statistics
Makes inferences about populations using data drawn from the sample
Population
All the inhabitants of a particular town, area, or country
Frequency distribution
A systematic arrangement of data values, with a count of how many times each value occurred in a dataset
Helps impose order on the data
Absolute frequency
Number of times things occur (count)
Relative frequency
the number of times an event occurs to all outcomes (percentage)
Class intervals
Values that are grouped into sets
The size of each class into which a range of a variable is divided, as represented by the divisions of a histogram or bar chart
Bar graph/chart
Used for nominal (and many ordinal) level variables
(X axis) that specifies categories (i.e., data values)
(Y axis) specifies either frequencies or percentages
Bars for each category drawn to the height that indicates the frequency or %
Do the bars of a bar graph/chart touch?
No
Pie chart
Used for: nominal and many ordinal (ranking) level variables
Circle divided into pie-shaped wedges corresponding to percentages for a given category or data value
All pieces add up to 100%
Place wedges in order with the biggest wedge starting at "12 o'clock"
Frequency polygon
Used for: interval- and ratio-level data
Similar to histograms, but instead of bars, a dot is used above score values to designate frequency/percentage
Better than histograms for showing shape of distribution of scores, and is usually preferred if variable is continuous
Modality
Concerns how many peaks (values with high frequencies, we have a great rise followed by a great fall) there are
Unimodal
1 peak
Symmetric distribution
The two halves of the distribution, folded over in the middle, are identical
Skewed distribution
Asymmetric (Skewed) Distribution: Peaks are "off center" and there is a tail trailing off for data values with low frequency
Positive (right) skew
Longer tail trails off to right (fewer people with high values, like for income)
Negative (left) skew
Longer tail trails off to left (fewer people with low values, like age at death)
Leptokurtic
Very thin, sharp peak (spread of data small)
Platykurtic
Flat peak (spread of data is large)
Normal distribution
(aka normal curve, bell-shaped curve, Gaussian distribution) is:
-Unimodal
-Symmetric
-Neither peaked nor flat (mesokurtic)
-Has a symmetric bell-shaped curve
Measures of Central Tendency
Mode, median, mean
Mode
The score value with the highest frequency; the most "popular" score
Age: 26 27 27 28 29 30 31
Mode = 27
Median
The score that divides the distribution into two equal halves
Also known as the 50th percentile:
50% are below the median, 50% above
Age: 26 27 27 28 29 30 31
N = 7 (odd number, one middle value)
Median (Mdn) = 28
Age: 26 27 27 28 29 30 31 32
N = 8 (even number, two middle values)
Median (Mdn) = (28+29)/2 = 28.5
Mean
The arithmetic average
Range
The difference between the highest and lowest value in the distribution
Weights (pounds):
110 120 130 140 150 150 160 170 180 190
The range here is 80 (190 - 110)
Standard deviation
An index that conveys how much, on average, scores in a distribution vary
Provides a "standard"—the SD indicates the average amount of deviation of scores from the mean
Variance
SD^2
Standard scores
(also called a z score) is a score expressed in standard deviation units, in relative distance from the mean
z = (X - M) ÷ SD
Unusual observations
Z-scores above +2 or Z-scores below -2
Contingency table
Crosstabulate (contingency) the frequencies of all categories of two variables in a two-dimensional frequency distribution
Crosstabulated variables should be nominal level (or ordinal level with a small number of categories)
Linear relationship
Occurs when there is a constant rate of change between the two variables
Scatterplot
Graphs the values of one variable on the X axis and the values of the second one on the Y axis of a graph
Coordinates of the X-axis is represented by the values of IV
Coordinates of the Y-axis is represented by the values of the DV
Scatterplot is a plot of ordered pairs of (x, y) points
Correlation coefficient
Aka Pearson's r
Measures the strength and direction of the linear relationship between two quantitative variables
Pearson's r
Pearson's r is unit-less
-1 < r < 1
The closer to -1, the stronger the negative linear relationship
The close to +1, the stronger the positive linear relationship
If r = 0, there is no LINEAR relationship
Positive relationship
Lines sloping from lower left to upper right
Negative relationship
Lines sloping from upper left to lower right
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