HLT 4307

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kppatel702
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36809
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HLT 4307
Updated:
2010-09-23 13:38:32
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measurement tech
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test 1 info
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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

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