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1. Descriptive Statistics
Descriptive statistics are used to organize and summarize information or data. They allow a researcher to provide a description of what actually exists in the data. Researchers use descriptive statistics to help us reduce large amounts of data to a more manageable size. Perhaps one of the most commonly used descriptive statistic is percentages.
2. Inferential statistics
Are often the next stage is data analysis. Are used when a researcher wants to make predictions. They use a smaller group, or a sample to compare data to larger groups.
3. Measures of central tendency:
Mean, Median, Mode
4. Mean:
The average
5. Median:
The middle
6. Mode:The value that occurs most in a set of numbers
7. Measures of variability:
range, sum of squares, variance, standard deviation.
8. Range
Highest number subtracted by lowest number
9. Sum of squares
SS= ex2- (ex)2/N
10. Variance
S2= SS/N-1
11. Standard Deviation:
SD= SS/N-1(square rooted)
12. Positive Skew:
a curve in which the tail of the curve is longer on the right side of the distribution. The mean will be greater than the mode.
13. Negative Skew
A curve in which the tail is longer on the left side. The mean will be less than the median or mode.
14. Measurement levels
Nominal, Ordinal, interval, ratio
15. Nominal
Nominal measurement is a simple classification. Numbers are selected and meaningless. Lowest level of measurement.
16. Ordinal
Rank-order measurement. Assigns numbers to things in such a way as to reflect relationships among things. Allows us to see hierarchal levels between groups.
17. Interval
Interval measurement identifies the distance between any two things that are measured. We assign numbers to things in such ways that the distance between any two things is measured. They are probably the most common measures used by scientists.
18. Ratio
A ratio scale has all of the characteristics of an interval scale, but in addition it has a true zero point as its origin. In ratio measurements, zero has an absolute value of its own.
19. Conceptualization and Operationalization
• Conceptualization- Clearly defining your variables.
• Operationalization: Process of measuring what you conceptualize. A good operationalization is like a recipe card. It tells you a very detailed procedure and what to do and when to do it.
20. Measurement:
the process of systematic observation and assignment of numbers to phenomena according to rules.
21. Isomorphism
means identity or similarity of form.
22. Likert Scale:
Disagree, Strongly disagree, agree, etc etc 5 terms.... ordinal
23. Hypothetical Variable
A variable that a researcher cannot directly observe, but is inferred from other variables that are observable and measured directly.
24. Reliability:
Stability and consistency over time. The accuracy that a measure has in producing stable, consistent measurement.
25. Scalar Reliability:
The reliability of individual research scales...... Good 1,2,3,4,5,6,7 Bad(circle whihc one fits best to individual personality)
26. Test-Retest Reliability
Testing the same thing on more than one occasion to make sure you get the same data.
27. Alternate forms reliability
Same thing as test retest, but you use each test as its own separate form of reliability.
28. Split half reliability
Involves computing two scores for each participant on the basis of one administration of the test. One of the scores comes from one half of the test and the other score comes from the other half.
29. Hoyt Analysis of variance reliability
internal reliability estimates. Another method of split halves.
30. Cronbachs alpha reliability
1,2,3,4,5 are flipped on the results... 5= good, 1=bad
31. validity
the degree to which the instrument measures what it is intended to measure.
32. improving reliability:
item construction, length of the instrument, administration of the test.

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 Author: Anonymous ID: 113527 Filename: comm 300 Updated: 2011-11-01 02:35:34 Tags: test Folders: Description: beam Show Answers:

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