2. Sampling Theory Concepts Population Target Population
Accessible Population Elements of a Population Sampling Criteria 3.
Sampling Criteria Characteristics essential forinclusion or
exclusion ofmembers in the targetpopulation Between the Ages of 18
& 45 Ability to speak English Dx of diabetes within last
month,or No Hx of chronic illness 4. Sampling Theory Concepts
Sampling Plans orMethods Sampling Error Random Variation Systematic
Variation 5. Sampling Error Random Variation The expected
difference in values thatoccurs when different subjects fromthe
same sample are examined. Difference is random because somevalues
will be higher and others lowerthan the average population values.
6. Sampling Error Systematic Variation (Bias) Consequence of
selectingsubjects whose measurementvalues differ in some
specificway from those of thepopulation. These values do not
varyrandomly around thepopulation mean 7. Sampling ErrorSampling
ErrorPopulation SamplePopulationMeanSampleMean 8. Sampling Theory
Concepts Sample Mortality Subject Acceptance Rate: Percentage of
individualsconsenting to be subjects Representativeness 9.
Representativeness Needs to evaluate: setting characteristics of
the subjects:age, gender, ethnicity, income,education distribution
of valuesmeasured in the study 10. Probability (Random)Sampling
Methods Simple Random Sampling Stratified Random Sampling Cluster
Sampling Systematic Sampling 11. Nonprobability (Nonrandom)Sampling
Convenience (Accidental)Sampling Quota Sampling Purposive Sampling
Network Sampling 12. Sample Size Factors influencing sample size
Effect size Type of study conducted Number of variables studied
Measurement sensitivity Data analysis techniques 13. Power Analysis
Standard Power of 0.8 Level of Significance alpha = .05, .01, .001
Effect Size .2 Small; .5 Medium; .8 Large Sample Size 14. Example
Sample A convenient sample of 55 adultsscheduled for first time
elective CABGsurgery without cardiaccatheterization, who had not
hadother major surgery within theprevious year, and who were
nothealth professionals met the studycriteria and were randomly
assignedto one of two instruction conditions... 15. Example Sample
Based on a formulation of 80% power, amedium critical effect size
of 0.40 for each ofthe dependent variables, and a significancelevel
of .05 for one-tailed t-tests means, asample size of 40 was deemed
sufficient totest the study hypotheses... 16. Example Sample The
study included a conveniencesample of 32 post-op Lung
Cancerpatients. A power analysis wasconducted to determine size.
Aminimum of 27 subjects was necessaryto achieve the statistical
power of 0.8and a medium (0.5) effect size at the0.05 level of
significance....Thesubjects were 25 men and 7 womenwith an age
range from 18-58 years(mean = 32.74).... 17. Critiquing the Sample
Were the sample criteriaidentified? Was the sampling
methodidentified? Were the characteristics ofthe sample described?
18. Critiquing the Sample Was the sample size identified? Was the
percent of subjectsconsenting to participateindicated? Was the
sample mortalityidentified? Was the sample size adequate? 19.
Concepts ofMeasurement 20. Measurement TheoryConcepts Directness of
Measurement Direct measurement Oxygen saturation,Temperature,
weight Indirect measurement Pain, depression, coping, self-care,
self-esteem 21. Measurement TheoryConcepts Measurement Error
Scoreobs = Scoretrue +Scoreerr Systematic error Random error Levels
of Measurement 22. Levels of Measurement Nominal data categorized,
but no order or zero (ex- gendernumbers) Ordinal categories with
order, but intervals not necessarilyequal and no zero (ex pain)
Interval equal intervals, but no true zero (ex- temp scales) Ratio
equal intervals with a true zero. These are realnumbers, for things
such as weight, volume, length. 23. Gender 1 = Male 2 = Female
(Nominal Data) 24. Likert Scale How often do you feel in control
ofyour life? (1) Never (2) Seldom (3) Often (4) Almost always 25.
Age How old are you (years)? What LOM? 26. Age How old are you?
25-34 35-44 45-54 55 or older What LOM? 27. Income 1 = under
$35,000 2 = $35-50,000 3 = $50 - 100,000 LOM? 28. What is
reliability? Reliability - is concernedwith how consistently
themeasurement techniquemeasures the concept ofinterest. 29. Types
of Reliability Stability -- isconcerned with theconsistency
ofrepeated measures ortest-retest reliability 30. Types of
Reliability Equivalence -- is focusedon comparing two versionsof
the same instrument(alternate forms reliability)or two observers
(interraterreliability) measuring thesame event. 31. Types of
Reliability Homogeneity -- addresses thecorrelation of various
itemswithin the instrument orinternal consistency;determined by
split-halfreliability or Cronbachs alphacoefficient. 32.
Inter-rater reliability Consistency in raters % = #
behaviorsperformed/total # ofbehaviors Values below 0.8 are
aproblem 33. What is validity? The extent to which aninstrument
reflects theconcept being examined. 34. MeasurementStrategies 35.
Physiologic Measures Physical MeasurementMethods EKG, BP SVO2,
Pulse Oximetry 36. Physiologic Measures Chemical/biochemical Blood
glucose SMA-24 PKU 37. Physiologic Measures Microbiological Smears
Cultures Sensitivities 38. Observational Measurement Unstructured
Observations Structured Observations Category Systems Checklists
Rating Scales 39. Interviews Unstructured Interviews Structured
Interviews Describing interview questions Pretesting the interview
protocol Training interviewers Preparing for an interview Probing
Recording interview data 40. Unstructured or Open ended: Tell me
about.. What has been your experiencewith.... What was it like to
hear youhave cancer? 41. Closed ended: Structured Response
alternatives fixed Which would you rather do,x or y? 42.
Measurement Strategies Questionnaires Scales Diaries 43.
Questionnaires Administration In person/on phone Self administered
Mail 44. Scales Rating Scales Likert Scales Semantic Differentials
Visual Analog Scales 45. Introductionto StatisticalAnalysis 46.
Normal Curve-3-3MeanMedianMode-2-2-1-10011223368.3%95.5%99.7%-2.58
-1.96 1.96 2.58 47. TailednessOne-Tailed Test- .05 Level of
SignificanceTwo-Tailed Test- .05 Level of SignificanceSignificantly
differentfrom meanSignificantly differentfrom meanSignificantly
differentfrom mean0.025 0.0250.05TailTail 48. Process for
Quantitative DataAnalysis Preparation of the Data for Analysis
Description of the Sample Testing the Reliability of the
Instrumentsfor the Present Sample Testing Comparability of Design
Groups Exploratory Analysis of Data Confirmatory Analyses Guided
byObjectives, Questions, or Hypotheses Post Hoc Analyses 49.
Cleaning Data Examine data Cross-check every piece of data with
theoriginal data If file too large, randomly check foraccuracy
Correct all errors Search for values outside the appropriaterange
of values for that variable. 50. Missing Data Identify all missing
data points Obtain missing data if at all possible Determine number
of subjects with datamissing on a particular variable Make
judgement - are there enoughsubjects with data on the variable
towarrant using it in statistical analyses? 51. Transforming Data
Transforming skewed data so that it is linear(required by many
statistics). Squaring each value calculating the square root of
eachvalue 52. Calculating Variables Involves using values from two
ormore variables in your data set tocalculate values for a new
variableto add to the data set. Summing scale values to obtaina
total score Calculating weight by heightvalues to get a value for
BodyMass Index 53. Statistical Tools Used to allow easy calculation
of statistics Computer-based tools allow rapid analysis
butsometimes too easy Must still know what each type of test is for
and how touse them Dont fall into the trap of using a test just
because it iseasy to do now Many papers appearing with questionable
tests justbecause a computer program allows the calculation 54.
Statistics Exercises Stat Trek http://stattrek.com/ Tutorial for
exercises Understand rationale for the selection of each test type.
Be prepared to utilize test if asked, and know major advantagesof
each main test. Miller Text (Chapter 21, Fifth Edition, pgs
753-792) Material very thorough. Many little-used tests described.
Read for idea of why other tests are available Dont get bogged down
in the details 55. Descriptive Statistics Describes basic features
of a data group. Basis of almost all quantitative data analysis
Does not try to reach conclusions (inferences), onlydescribe.
Provide us with an easier way to see and quickly interpretdata 56.
Descriptive Statistics Data Types Based on types of measurement
Measurement scales can show magnitude, intervals, zero point,
anddirection Equal intervals are necessary if one plans any
statistical analysis ofdata Interval scales possess equal intervals
and a magnitude Ratio scales show equal intervals, magnitude and a
zero point Ordinal scales show only magnitude, not equal intervals
or a zeropoint Nominal data in non-numeric (not orderable)
whereasordinal data is numeric and can be ordered but not basedon
continuous scale of equal intervals 57. Descriptive Statistics Goal
of use is to be able to summarize the data in a waythat is easy to
understand May be described numerically or graphically Describe
features of the distribution Examples include distribution shape
(skewed, normal(bell-shaped), modal, etc), scale, order, location
58. Descriptive Statistics Location Statistics How the data falls
Examples would be statistics of central tendency Mean Average of
numerical data x / n Median Midpoint of data values Value of data
where 50% of data values is above and 50% below (ifnumber of data
points is even, then the middle two values are averaged) Mode Most
frequent data value May be multi-modal if there is an identical
number of max data values 59. Descriptive Statistics Location
Statistics Data outliers may need to be accounted for and
possiblyeliminated This can be done by trimming or weighting the
mean toeffectively eliminate the effect from outliers 60.
Descriptive Statistics Count Statistics One of the simplest means
of expressing an idea Works for ordinal and nominal data 61.
Descriptive Statistics Statistics of Scale Measures how much
dispersal there is in a data set(variability) Example statistics
include sample range, variance,standard deviation (the square root
of the variance), SEM(SD/sq root of N) Outliers can influence
variance and standard deviationgreatly, so try to avoid their use
if there are lots of outliersthat can not be weighted out 62.
Descriptive Statistics Distribution Shape Statistics Determines how
far from normal the distribution of datais based on normal
distribution shapes (Gaussian) Skewness measures how tailed the
data distribution is(positive to right, negative to left) Kurtosis
measures whether the tail is heavy or light 63. Inferential
Statistics Attempts to come to conclusions about a data set that
arenot exactly stated by the data (inferred) Many tests use
probability to help determine if datapoints to a likely conclusion.
Often used to compare two groups of data to see if theyare
statistically different Often used to decide whether or not a
conclusion one istrying to reach from the data set is reliable
(withinstatistical probability) 64. Inferential Statistics Simplest
form is the comparison of average data betweentwo data sets to see
if they are different Students t-test is often used to compare
differencesbetween 2 groups Usually one control group and one
experimental Should be only one altered variable in
experimentalgroup 65. Inferential Statistics Most common
inferential statistical tests belong to theGeneral Linear Model
family Data is based on an equation in which a wide variety
ofresearch outcomes can be described Problems with these types of
analysis tools usually comesfrom the wrong choice of the equation
used Errors in the wrong equation used can result in the
dataconclusions being biased one way or the other, leading
toaccepting or rejecting the null hypothesis wrongly 66.
Inferential Statistics Common Linear Model tests include: Students
t-test Analysis of variance (ANOVA) Analysis of covariance (ANCOVA)
Regression analysis Multivariate factor analysis 67. Inferential
Statistics Type of research design used also determines thetype of
testing which can be done: Experimental analysis Usually involves
comparison of one or more groups against acontrol, and thus t-test
or ANOVA tests are the most commonlyused Quasi-experimental
analysis Typically lack a control group, and thus the random
analysis that isusually used to assign individuals to groups These
types of analysis are much more complex to compensate forthe random
assignments
