Statistics-Biomedical Sciences.txt

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Statistics-Biomedical Sciences.txt
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2013-03-31 20:32:58
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Statistics
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Basic Statistics
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    • author "Jeanne"
    • tags "Biomed, Stats"
    • description "First Exam for Basic Statisrics ( Masters of Science in Healthcare Informatics)"
    • fileName "Statistics-Biomedical Sciences"
    • freezingBlueDBID -1.0
    • What is statistics?
    • A field of study that involves methods for describing and analyzing data. It reduces uncertainty and provides for better decision making.
  1. What is a population
    Universe of cases or subjects of interest to the analyst. People, thins or concepts
  2. What is a sample
    An observable subset of the population. It needs to mirror the population.
  3. What are some types of samples?
    Random and Non Random
  4. What is a random sample
    All units have an equal chance of being included in the sample.
  5. What are 3 ways to obtain a random sample?
    • 1. Using a table of random numbers
    • 2. Computer generated random samples
    • 3. Software selected random sample
  6. Describe a simple random sample.
    • -Assign all elements a number, in a class of 25 - assign 1 - 25
    • -Determine the sample size example: 5
    • -Use a table to assign random numbers to provide 5 random numbers between 1 and 25
  7. Describe systematic sample
    • -Produce names of population
    • -Determine sample size (5)
    • -Divide the total (25) by the sample size (5) = 5
    • -Take every 5th name on the list for inclusion in the sample
    • -If the 5th person refuses, the analyst must begin the count to 5 again.
  8. Describe a stratified sample
    Divide the number of STRATA (groups) that share similar characteristics. Draw random samples from each Stratum.
  9. Cluster Sampling is?
    Sampling based on selecting clusters from a population and then sampling from those clusters. Examples: geography - rural, suburbs, city
  10. Describe the differences between stratified and cluster sampling
    Cluster samples only include a subset of the clusters. Stratified samples include all of the strata. Stratified samples allow for more precision.
  11. Name some non random sampling types
    • 1. Convience samples - surveying the first 10 people in a parking lot
    • 2. Volunteers- American Idol
    • 3. Judgemental sample- a sample based on expert judgment
    • 4. Quota sample - convience sample designed to provide a certain distribution
  12. Define sampling error
    The difference between the sample and the larger population that is due to pure random chance .
  13. What is true of sample error.
    As sample size increases sampling error decreases .
  14. Define sampling bias
    The differences between a sample and the population that are not do to pure random chance.
  15. Describe a fact about sampling bias.
    Unlike sampling error, sampling bias will not decrease as your sampling number increases .
  16. Describe some sources of selection bias
    • -A group that is under represented in your samples.
    • -A group that fails to respond to your survey non response bias
    • -A group that self select so as the sample, American Idol.
  17. Define measurement error
    Inaccuracy or miscalculation of the observation , caused by unclear questions, leading questions, questions containing social desirability componant.
  18. Define Validity
    Does the instrument measure what it intended to measure.
  19. Define Reliability
    Does the instrument provide constant results over repeated measurements.
  20. Name and describe 4 dimensions to validity
    • 1. Face - does the anayst have confidence in the measuring instrument
    • 2. Content- concerned with the sample population representatives-
    • 3. Correlative - the results have a high correlation to other established measures of validity
    • 4. Predictive- the resilts should be able to successfully predict outcomes gre = success in a graduate program
  21. Define external validity
    Results that can be readily generalized to the larger population are said to have external validity.
  22. Define internal validity
    Did I measure what I claimed to measure by eliminating all confounding variables
  23. List and describe 8 threats to internal validity
    • 1. History - external events that produce an effect that can be confused with the outcome. school program success vs economic boom happening at same time
    • 2. Maturation - internal factors that can be confused with outcome - treated allergies that resolve over time, due to tx or due to growth of child
    • 3. Testing- measuring a person that can produce the effect confusing the outcome- Stalins arrival improves productivity
    • 4. Instrumentation - changes in the measurement tool
    • 5. Statistical regression to the mean - selection of a group due to their deviance from the mean - odds are that next measurement that group will regressed to
    • mean.
    • 6. Selection bias
    • 7. Experimental mortality- subjects dropping out of study will change the composition of the sample.
    • 8. Selection-Maturation Interaction -any bias in selection will interact with maturation to produce a greater effect than maturation alone
  24. Name 3 research design techniques
    • 1. Pre-experimental - policy is changed and later a decision is made to evaluate the policy
    • 2. Quasi Experimental - uses a comparison group. Ex: impact of affirmative action on female employment in shipyards.
    • 3. Experimental -includes randomization componant. Participants randomly selected and randomly assigned to experimental or control group.
  25. Describe an example of the paradox of internal and external validity
    Real world clinical trials often may have a drug that is valid in a controlled setting (high internal value) but not effective in the real world where patients don't follow direction (low external validity)
  26. List 4 levels of measurement
    • 1. Nominal - catagorizing information. Hair Color: 1= blond 2= brunette 3= other
    • 2. Ordinal - ranked in order of some rype of continuum. 1= strongly agree 2= agree 3= neutral 4= disagree 5= strongly diagree
    • 3. Interval - regular numbers where distance between the numbers is the same and all numbers anchored by an arbitrary zero - IQ, Temp, Test scores
    • 4. Ratio Scale distance between points is equal and anchored by a non arbitrary zero. Hourly wage, height, weight, age, miles driven in a day
  27. Give some examples of Nominal data
    • -Gender
    • -Ethnicity
    • -Marital status
  28. Give some examples of Ordinal Data
    • -Movie ratings
    • -Scio economic status
    • -Rating of meat in the store
    • -Rank order of anything
  29. Name some examples of Interval Data
    • -Degrees F OR C
    • -Most personality measures
    • -Intelligence scores
  30. List some examples of Ratio Data
    • -Annual income in dollars
    • -Distance as measured in miles, inches, centimeters etc..
  31. What arithmetical operation is used for Nominal Data
    Counting
  32. What arithmetical operations can be used for Ordinal Data?
    Gretaer than or less than
  33. What arithmetical operation is permitted with Interval Data?
    Addition and subtraction of the scale values
  34. What arithmetical operation is permitted with Ratio Data?
    Multiplication and division of scale values
  35. Name the measures of central tendency begining with the most commonly used.
    • 1. Mean
    • 2. Mode
    • 3. Median
    • 4. Trimmed Mean
  36. Define Mode
    A measurement of central tendoncy that is equal to the score that occurs most often in the distribution.
  37. Define Median
    The score that divides the distribution in half. =(n + 1) / 2 will identify the positon of the median
  38. Define the Mean
    The arithmetic average of scores. Sum the scores and divide by the number of scores.
  39. Which measures of central tendoncy are not useful is statistical decsion making but may be helpful interms of describing them
    Mode and Median
  40. List three catagories of samples
    • 1. Probability samples -most often recommended because they facilitate inference that can be made to general public
    • 2. Judgement samples
    • 3. Chunks
  41. What is the difference between cluster and stratified sampling.
    The huge and appreciable difference between stratified andcluster methods is that when the groups are created, some members from each group or strata are selected. With a cluster, when clusters are created, the whole population of some of the clusters are used.
  42. Define nominal measurement
    A level of measure where something is counted. It has no numerical value. Yes/No, Female/Male, Hair Color, Vehicle: 1=ford 2= chevy 3=volvo
  43. Define ordinal data
    Data that can be expressed as > or <. 1= strongly agree 2=agree 3= disagree 4= strongly disagree , salary range
  44. Define interval data
    Interval data does not have an absolute zero but the intervals between each is the same. Example degrees C or F, intelligence tests
  45. Define ratio scale data
    Has an absolute zero like degrees kelvin, wage, miles driven, weight, height.
  46. What components of research design?
    • Qualitative or Catagorical will be different in quality but not in quantity and these unordered groups are generally used to observe not measure. Focus groups.
    • Quantitative or Measurement takes on a numerical value. Age, weight, test scores. Tells how much.
  47. Define validity
    Does the instrument measure what it is intended to measure
  48. Define reliability
    Does the instrument provide consistent results
  49. What is true of validity and reliability
    Validity implies reliability. Reliability doesnt necessarily imply validity
  50. Name 4 dimensions of validity
    • Face
    • Content
    • Correlational
    • Predictive
  51. External validity means?
    Can the findings be genralized to the larger population.
  52. A policy is implemented and later evaluated. What is this called?
    Pre-experimental design techniques. X (treatment or factor) 》 O ( observation or measurement)
  53. Define quasi experimental design technique
    The analyst adds a a level of comparison to the pre - experimental design , X 》 O ( treatment group) and O 》 O ( comparision group).

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