1.Sampling

2. Sampling Theory Concepts Population Target Population Accessible Population Elements of a Population Sampling Criteria 3. Sampling Criteria Characteristics essential forinclusion or exclusion of members in the target population 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 or Methods Sampling Error Random Variation Systematic Variation 5. Sampling Error Random Variation The expected difference in values thatoccurs when different subjects from the same sample are examined. Difference is random because somevalues will be higher and others lower than the average population values. 6. Sampling Error Systematic Variation (Bias) Consequence of selectingsubjects whose measurement values differ in some specific way from those of the population. These values do not vary randomly around the population mean 7. Sampling Error Sampling ErrorPopulationSamplePopulation MeanSample Mean 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 adults scheduled for first time elective CABG surgery without cardiac catheterization, who had not had other major surgery within the previous year, and who were not health professionals met the study criteria and were randomly assigned to one of two instruction conditions... 15. Example Sample Based on a formulation of 80% power, a medium critical effect size of 0.40 for each of the dependent variables, and a significance level of .05 for one-tailed t-tests means, a sample size of 40 was deemed sufficient to test the study hypotheses... 16. Example Sample The study included a convenience sample of 32 post-op Lung Cancer patients. A power analysis was conducted to determine size. A minimum of 27 subjects was necessary to achieve the statistical power of 0.8 and a medium (0.5) effect size at the 0.05 level of significance....The subjects were 25 men and 7 women with 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 participate indicated? Was the sample mortality identified? Was the sample size adequate? 19. Concepts of Measurement 20. Measurement Theory Concepts Directness of Measurement Direct measurement Oxygen saturation, Temperature, weight Indirect measurement Pain, depression, coping, selfcare, self-esteem 21. Measurement Theory Concepts 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? 25-34 35-44 45-54 55 or older 26. Income 1 = under Rs-35,000/ 2 = Rs-35-50,000/ 3 = Rs-50 - 100,000/- 27. What is reliability? Reliability - is concernedwith how consistently the measurement technique measures the concept of interest. 28. Types of Reliability Stability -- is concerned with the consistency of repeated measures or test-retest reliability 29. Types of Reliability Equivalence -- is focused on comparing two versions of the same instrument (alternate forms reliability) or two observers (interrater reliability) measuring the same event. 30. Types of Reliability Homogeneity -- addresses the correlation of various items within the instrument or internal consistency; determined by split-half reliability or Cronbachs alpha coefficient. 31. Inter-rater reliability Consistency in raters % = # behaviorsperformed/total # of behaviors Values below 0.8 are a problem 32. What is validity? The extent to which aninstrument reflects the concept being examined. 33. Measurement Strategies 34. Physiologic Measures Physical MeasurementMethods EKG, BP SVO2, Pulse Oximetry 35. Physiologic Measures Chemical/biochemical Blood glucose SMA-24 PKU 36. Physiologic Measures Microbiological Smears Cultures Sensitivities 37. Observational Measurement Unstructured Observations Structured Observations Category Systems Checklists Rating Scales 38. Interviews Unstructured Interviews Structured Interviews Describing interview questions Pretesting the interview protocol Training interviewers Preparing for an interview Probing Recording interview data 39. Unstructured or Open ended: Tell me about.. What has been your experiencewith.... What was it like to hear youhave cancer? 40. Closed ended: Structured Response alternatives fixed Which would you ratherdo, x or y? 41. Measurement Strategies Questionnaires Scales Diaries 42. Questionnaires Administration In person/on phone Self administered Mail 43. Scales Rating Scales Likert Scales Semantic Differentials Visual Analog Scales 44. Introduction to Statistical Analysis 45. Normal Curve Mean Median Mode 68.3%95.5%-3 -3-2 -1 -2 -1 -2.58 -1.9699.7%0 01 12 2 1.963 3 2.58 46. Tailedness Significantly different from meanSignificantly different from mean0.0250.025TailTailTwo-Tailed Test- .05 Level of Significance0.05Significantly different from meanOne-Tailed Test- .05 Level of Significance 47. Process for Quantitative Data Analysis Preparation of the Data for Analysis Description of the Sample Testing the Reliability of the Instruments for the Present Sample Testing Comparability of Design Groups Exploratory Analysis of Data Confirmatory Analyses Guided by Objectives, Questions, or Hypotheses Post Hoc Analyses 48. Cleaning Data Examine data Cross-check every piece of data with the original data If file too large, randomly check for accuracy Correct all errors Search for values outside the appropriate range of values for that variable. 49. 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 to warrant using it in statistical analyses? 50. Transforming Data Transforming skewed data so that it is linear(required by many statistics). Squaring each value calculating the square root of eachvalue 51. Calculating Variables Involves using values from two or more variables in your data set to calculate values for a new variable to add to the data set. Summing scale values to obtaina total score Calculating weight by height values to get a value for Body Mass Index 52. 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 to use them Dont fall into the trap of using a test just because it is easy to do now Many papers appearing with questionable tests just because a computer program allows the calculation 53. 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 advantages of 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 54. 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 interpret data 55. Descriptive Statistics Data Types Based on types of measurement Measurement scales can show magnitude, intervals, zero point, and direction Equal intervals are necessary if one plans any statistical analysis of data 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 zero point Nominal data in non-numeric (not orderable) whereasordinal data is numeric and can be ordered but not based on continuous scale of equal intervals 56. 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 57. Descriptive Statistics Location Statistics How the data falls Examples would be statistics of central tendency Mean Median Average of numerical data x/n Midpoint of data values Value of data where 50% of data values is above and 50% below (if number 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 58. Descriptive Statistics Location Statistics Data outliers may need to be accounted for and possibly eliminated This can be done by trimming or weighting the mean to effectively eliminate the effect from outliers 59. Descriptive Statistics Count Statistics One of the simplest means of expressing an idea Works for ordinal and nominal data 60. 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 deviation greatly, so try to avoid their use if there are lots of outliers that can not be weighted out 61. Descriptive Statistics Distribution Shape Statistics Determines how far from normal the distribution of data is 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 62. 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 data points to a likely conclusion. Often used to compare two groups of data to see if they are statistically different Often used to decide whether or not a conclusion one is trying to reach from the data set is reliable (within statistical probability) 63. 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 differences between 2 groups Usually one control group and one experimental Should be only one altered variable in experimental group 64. 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 comes from the wrong choice of the equation used Errors in the wrong equation used can result in the data conclusions being biased one way or the other, leading to accepting or rejecting the null hypothesis wrongly 65. Inferential Statistics Common Linear Model tests include: Students t-test Analysis of variance (ANOVA) Analysis of covariance (ANCOVA) Regression analysis Multivariate factor analysis 66. Inferential Statistics Type of research design used also determines the type of testing which can be done: Experimental analysis Usually involves comparison of one or more groups against a control, and thus t-test or ANOVA tests are the most commonly used Quasi-experimental analysis Typically lack a control group, and thus the random analysis that is usually used to assign individuals to groups These types of analysis are much more complex to compensate for the random assignments