# HLT 4307

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1. Measurement:
The act of collection of information on which a decision is made
2. Evaluation
The use of measurement in making decisions
3. Law
Concise statement of fact that has been proven time and time again, generally accepted as true and universal
4. Theory
an explanation of a set of related observations that is based upon proof that has been verified
5. Hypothesis
attempt to explain some basic observations before precise data has been rigorously collected and analyzed
6. Quantitative
• deals with numbers
• can be measured

ex. length, speed
7. Qualitative
• descriptions
• can be observed

ex. yellow, soft
8. Statistics
a collection of methods for planning experiments, obtaining data, then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data
9. Population
complete collection of all elements to be studied (scores, people)
10. Census
the collection of data from members of a population
11. Sample
a sub collection of elements drawn from a population
12. Statistic
a numerical measurement describing some characteristic of a sample
13. Parameter
a numerical measurement describing some characteristic of a population
14. Descriptive Statistic
summarize or describe characteristics of a known set of data
15. Inferential Statistics
use sample data to make inferences (or conclusions and predictions) about a sample

correlation or experimental designs
16. Important characteristics of data
Center: value that shows the middle of data set is

Variation: a measure of the amount that values vary among themselves

Distribution: nature or shape of distribution of data (bell shaped, uniform, or skewed)

Outliers: sample values that are far from the majority of other values

Time: changing characteristics of the data over time
17. Measure of Central Tendency
value at the center or middle of a data set

• median - use when there are extreme values
• mode - when data is categorical
• mean - every other time
18. Variability
how different scores are from the mean (spread, dispersion)

• range
• standard deviation
• variance
19. Range
• Max - Min
• used to get a general estimate of different scores are from either other
20. Exclusive range
Highest score - lowest
21. Inclusive range
Highest - lowest + 1
22. Standard deviation
measure of variation of values about the mean

s can increase dramatically with inclusion of outliers

units are the same as data

larger sd, greater the variance
23. Variance
the same thing as standard deviation except squared
24. Descriptive
• X is Y
• how things are
• most common type of study
• observe and measure specific characteristics without attempting to modify the subjects that are being studied
25. Correlational
• x is related to y
• how things are in relation to other things
• used most commonly in health science studies
• observations not manipulated but related to each other
26. Experimental
x causes y

• how things are and how they got that way
• hard to do well; apply treatments and observe effects
• used sometimes in evaluation but usually to explain descriptive evaluations
27. Methods of sampling
• Random - equal chance of being selected
• Systematic - every nth element in a population (ex. every third person)
• Convenience - data easy to get
• Stratified - but into subgroups then choose randomly from the group
• Cluster - divide population into clusters then choose random clusters and use all the population within the cluster
28. Experimental Designs
Cross sectional - all data observed, measured and collected at ONE point in time

Retrospective - data collected from the past

Prospective - data collected in the future from groups (cohorts) sharing common factors
29. Confounding
Occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors

• Plan an experiment to avoid confounding
• Can avoid it by:
• Binding - participants dont know whether they are receiving treatment or placebo
• Matching - participants with similar characteristics
• Randomized Controlled Trial - randomly assign to each experimental group
30. Frequency Distribution
• lists data values (individually or groups of intervals)
• interval is called class or bin; helpful for large data sets
31. Skewness
Distribution extends to one side more then the other

• Skewed to the left (negatively)
• Skewed to the right (positively)
32. Histogram
• a type of graph that portrays the nature of a data distribution
• Normal distribution has a bell shape
33. Kurtosis
Has to do with how flat or peaked a distribution appears

• Platykurtic - more flat
• Leptokurtic - more peaked
34. Charts
• Column - to compare, bars horizontal
• Bar - same except vertical
• Line - to show trend
• Pie - to show proportions
35. Correlation
• relationship between two variables
• can be generated for predicting the value of one variable given the value of the other variable
• good for data that comes in pairs
36. Experimental research
• aims to find casual mechanisms and determine predictability
• always at least one independent variable and one dependent variable
• relationships can be bivariate or multivariate
37. Correlation vs. Experimental
• Correlation:
• investigates linear relationship between two variables
• continuous variables
• data can be graphically presented
• neither is truly the ind. or dep. variable
• called a bivariate relationship
• no causation
38. Correlation coefficient (r)
• a numerical measure of the strength of the relationship between two variables representing quantitative data
• r is in between -1 and 1
• value of r does not change even if units change
• measures strength of a linear relationship only
39. Homoscedasticity (homogeneity or variance)
• variance or errors are randomly and evenly distributed
• variance or errors on one variable are not correlated with variance or errors on another variable
40. Requirements for r
• Sample of pair x,y is a random sample of independent quantitative data
• approximate straight line pattern
• outliers need to be removed if their known to be errors
41. Common errors involving correlation
• Causation: wrong to conclude that correlation implies casualty
• Averages: averages suppress individual variation and may inflate the correlation coefficient
• Linearity: there may be some relationship between x and y even when there is no linear correlation
42. Measurement
consists of rules for assigning numbers to (objects) in such a way as to represent quantities of attributes

most measurement is indirect
43. Variables of interest
• what do you want to know and how an you know it
• empirical or operational definitions: what can be measured that bests reflects what we want to measuere
44. Classical test theory: O = T + E
• Observed score: actual score n a test
• True score: theoretical reflection of the actual amount of a trait or characteristic an individual possesses
• Error score: part of the score that is random
45. True Score
• The actual amount of the attribute you want to measure (ex. true dietary intake)
• Assumption: the construct is real and exists much like blood level or atomic weight if only we could measure it accurately
46. Errors
• Error - did not intend to measure that messed up the score
• Systematic error - repeatedly occurs and affects scores predictably
• Non systematic error - unpredictable and varies
47. Levels of Measurement
• Nominal - characteristic, names, least precise measure, mutually exclusive (cant be both)
• Ordinal - order, ranking
• Interval - where a test or assessment tool is based on something we can talk about how much higher performance is compared to a lower one
• Ratio - characterized by the presence of an absolute zero; absence of any of the trait that is being measured
48. Reliability
the degree to which scores are: free from errors of measurement; consistent, or stable across a variety of conditions

• types of reliability:
• Test retest reliability
• Interrater reliability
• Internal consistency reliability
• Parallel forms reliability
49. Test-retest reliability
used when you want to examine whether a test is reliable over time (do it again in time by the same people) then find the correlation efficient when comparing scores aka correlation on a test given at two diff times

ex. same test is taken in july and january by the same people

• longer times require greater stability
• affected by change, carry over effects
50. Interrater Reliability
• measure that tells you how much two raters agree on their judgments of some outcome
• correlation of scores measured by two different observers or raters

number of agreements/ number of possible agreements
51. Internal consistency reliability
used when you want to know whether the items are consistent with one another in that they represent one dimension, construct, or area of interest

• ex. different test forms
• a function of the relationship between items on a scale and number of items
52. Parallel forms of reliability
• when you wan to examine the equivalence or similarity between two different forms of the same test (correlation of scores between two different versions of the test)
• ex. studying two different things same method

then find correlation coefficient
 Author: kppatel702 ID: 36809 Card Set: HLT 4307 Updated: 2010-09-23 17:38:32 Tags: measurement tech Folders: Description: test 1 info Show Answers: