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*organizing data
*manipulating numbers to characterize and make inferences
*what "story" do the numbers tell |
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| Consumer of Research need to know |
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*which stats to answer RQ/hypothesis
*how the stat works/underlying logic
*how to interpret the statistic |
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| a condition or characteristic that can have different values (independent/dependent) |
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| a possible number or category that a score can have |
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| a particular person's value on a variable |
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(nominal and ordinal)
use discrete variables (whole units/#'s) |
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(interval and ratio)
use continuous variables
ex: age, time |
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Definition
-samples randomly drawn from population
-population parameters
-variances of samples are roughly equal |
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| Nonparametric Assumptions |
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-used when normally and homogeneity of variance are not satisfied
-use with small samples |
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-subjected to arithmetic manipulations
-interval
-ration |
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necessary in all research
-summarize/organize a group of numbers from a sample in a research study
-describe characteristics
-describes basic outcome of the study (number of participants, mean scores, range of scores, frequency, mode, median, variance [SD]) |
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-used to generalize finding to a larger population
e.g. T-test, chi-square, ANOVA, MANOVA, factor analysis
-draw conclusions/make inferences that go beyond the numbers
-infer from sample to population |
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Definition
| -identify relationships between multiple variables for inference or prediction |
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-get rid of incomplete scores
-examine outliers
-look at similarity in groups
*did the two groups start out at the same baseline?
*were the groups homogenous? |
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| rank from lowest to highest |
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Definition
-create frequency distributing table
-create frequency distribution chart |
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| Central tendency of the distribution |
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Definition
-where do the scores fall
-what is the deviation from the mean
-symmetry of the curve |
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| Discrete Variables: non-continuous |
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| Discrete Variables: continuous |
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Definition
-frequency polygon
-line graph
-histogram (bar without space)
-stem and leaf |
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-provide a listing of individuals having each of the different values for a particular variable
ex: stress ratings of 151 students |
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| Steps for making a frequency table |
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Definition
1. make a table showing how many times each value on your list is used
2. figure the percentage of scores for each value
3. figure the cumulative percent |
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| Unimodal frequency distribution |
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Definition
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| Bimodal frequency distribution |
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| Rectangular frequency distribution |
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| Controversies and Limitations |
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-failure to use equal interval sizes
-exaggeration of proportions
ex: 3 students in study...100% of students |
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| Measures of Central Tendency= Mean |
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=sum of X/N
- preferred measurement
-varies less than median or mode |
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=middle number
-used if distribution is not symmetric
-not sensitive to skewed scores- simply the middle |
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=most frequent
-reports most common score
-seen best in frequency distribution
-most useful with categorical data (nominal variables) |
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-distance in the distribution
-how much do scores deviate from the mean
=core of statistics! |
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=difference between high and low
ex: 99-57= 42 |
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=value where%s of scores fall above ore below score
ex: 25th |
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| =range of the middle 50% of scores (ignoring outliers) |
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| =the sum of squaring difference between each score and mean (eliminates problems with negative numbers) |
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| =variability of individual scores from the mean |
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| =the average variability of individual scores of the mean |
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-relationships between two or more variables (how do they relate)
- contingency tables (categorical/nominal data relationships [age and gender])
-correlational analysis (extent to which variables are related) |
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-direction and magnitude of relationships
*direction:
-positive correlation- the two variables both increase or decrease in the same direction
-negative correlation- the two variables move in opposing directions (one increases/one decreases) |
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strength of relationship (-1 to +1)
-1: perfect negative
+1: perfect positive |
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| Ordinal data/non-parametric |
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| changing ordinal data to interval parametrics |
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