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Overview of methods of analyzing qualitative data

Overview of methods of analyzing qualitative data

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  • So we have already collected our data. But all these are just raw information. For it to be of any use to us we have to apply analytic methods to the data.What is our main consideration? Of course, it is the OBJECTIVES of our study
  • - have respondents answered all questions? - are there any inconsistencies? - verify identification numbers - systematize your coding system
  • CODING - Assigning numerical values to research variablesAfter coding you are ready to enter the RAW data into your computer softwareEncoding research data into a computer facilitates computation of statistical testing (through selected software)
  • This is an example of the data encoding using the 2004 study by Salvacion on the stress profile of students in the UP College of Dentistry The researcher used questionnaires, tests, and inventoriesThe questionnaire asked 149 students basic demographic data, like !D#, year level, sex, civil status and residence These were some of the variables the researcher hypothesized to be related to the stress profile of the studentsSince ID #s and year level are real #s they could be entered into EXCEL without any codinSex can be coded as 1 for male, 2 for female ; Civil status is coded as 1 for single, 2 for maried, etc.These numbers can now be entered in the excel spreadsheetsheetSo lets take entry for respondent with ID # 1 who is a 3rdyr student, male, single and lives in a dormitory within Ermita.
  • QUANTITATIVE VARIABLES categories can be measured and ordered according to quantity/amount;values can be expressed NUMERICALLY (discrete -whole #s or continuous- fractions and decimals)QUALITATIVE VARIABLES - categories are used as labels to distinguish one group from another (not a basis for saying that one group is greater or less, higher or lower, better or worse than another)
  • It is important to distinguish the type of variable one is dealing with- major determinant of the type of statistical technique applied to the data- It also determines the type of graph that can be constructed as well as the- statistical measure that can be computed from a given set of data
  • In the next slidewe will review the formula for getting the variance for a population and for a sample
  • MEAN (X) – Computed by dividing the sum of all scores by the number of scoresMEDIAN (Md)- 50th percentile. Point above and below which half of the scores fallMODE (Mo) – most frequently occurring score in the distributionRANGE – difference between the highest and lowest scores plus 1STANDARD DEVIATION - indicates how much scores are spread around the mean - Square root of the variance
  • n – 1 because we are using a sample not a population
  • Normaly distributed population
  • b. T-test for paired data - identify statistically significant changes in a single group (e.g. pre-test and post-test) - or between matched groups ( e.g. pre-test scores of matched members of 2 groups, experimental and comparison)
  • - to determine significant difference among 3 or more group means (1 variable) Ex. Posttest scores of students to compare effectiveness of 3 instructional strategies
  • Randomized post-test only control design N= 168 2ndyr med students + 23 2nd yr physician assistant students randomly divided into 4 grps given the different instructional methods Students’ knowledge and skills were assessed after instruction to determine any significant difference among the groups through ONE-WAY ANOVA
  • Ex. When Pre-test means of groups are significantly different from each other ANCOVA can be used to adjust pretest scores so they can be treated as identical
  • Ex. Survey of pharmacists to determine work patterns and whether other factors (age, gender, # years in work force) affected the work patterns (Knapp et al. 1992)
  • Quasi-experimental design
  • For nominal and ordinal variablesApplicable when underlying assumptions for parametric tests are not metPARAMETRIC tests – for interval and ratio variables assuming that: - sample was drawn from a normally distributed population - if two groups are to be analyzed they have the same varianceUseful for small sample size

Analyzing quantitative Presentation Transcript

  • 1. ANALYZINGQUANTITATIVEDATA HP 299 REPORT Cristina M. Laureta
  • 2. DATA ANALYSIS Beforethe collected data can be utilized, appropriate analytic methods must be applied to meet the users need for information Mainconsideration – OBJECTIVES for which the data were collected (Mendoza, et al Foundations of statistical analysis for the health sciences 2009)
  • 3. Organization and Presentation of Data Collected data – questionnaires, examination papers, rating scales, interview transcription, secondary data Start by thoroughly reviewing all accomplished instruments and other data - have respondents answered all questions? - are there any inconsistencies? - verify identification numbers - systematize your coding system
  • 4. Coding Assignnumerical values to research variables Enter the RAW data into your computer software Encoding data into a computer facilitates computation of statistical testing Ex. Microsoft Excel
  • 5.  After entering the data you are now ready to process them Sample table entry
  • 6. BIOSTATISTICS deals with both qualitative andquantitative data; either constants or variablesCONSTANT VARIABLEphenomenon whose value phenomenon whose values/remains the same categories cannot be from person to person, predicted with certainty from time to time, from place to place# minutes in an hour age of gestationpull of gravity smoking habitspeed of light attitudes towards certain issues weight educational attainment (Mendoza, et al Foundations of statistical analysis for the health sciences 2009)
  • 7. VARIABLESQUANTITATIVE QUALITATIVEcategories can be categories are used as labelsmeasured and ordered to distinguish one group fromaccording to quantity/ anotheramount; values can beexpressed numerically(discrete or continuous)birth weight sexhospital bed capacity urban-ruralarm circumference religionpopulation size region disease status occupation (Mendoza, et al Foundations of statistical analysis for the health sciences 2009)
  • 8. Types of scalesNominal Ordinal Interval Ratio(QL) (QL/QN)numbers refer can be ranked exact distance zero point isto categories, or ordered between 2 fixedgroups, labels categories canof data be determined; zero point is arbitrarymeasurement disease temperature, weight, scale set for severity (mild, IQ moneydata collection moderate, severe)
  • 9. It is important to distinguish the typeof variable one is dealing with - major determinant of type of statistical technique - type of graph that can be constructed - statistical measure that can be computed (Mendoza, et al Foundations of statistical analysis for the health sciences 2009)
  • 10. DESCRIPTIVE STATISTICS Describe the characteristics of the members of one group No attempt to compare or relate these to the characteristics of another group Measures of central tendency and variation
  • 11. MEASURES OF CENTRALTENDENCY Methodof compressing a mass of numerical data for better comprehension and description of what it tends to portray MEAN, MEDIAN, MODE – “typical “ or average values which may be utilized to represent a series of observations (Mendoza, et al Foundations of statistical analysis for the health sciences 2009)
  • 12. MEASURES OF CENTRALTENDENCYA. MEAN (X) – arithmetic mean thatrepresents a set of scores with a singlenumberComputed by dividing the sum of all scoresby the number of scores
  • 13. MEASURES OF CENTRALTENDENCYB. MEDIAN (Md)- 50th percentile - Point above and below which half of the scores fall - Better choice than MEAN if there are extreme values
  • 14. MEASURES OF CENTRALTENDENCYC. MODE (Mo) – most frequently occurring score in the distribution
  • 15. MEASURES OF SPREAD ORDISPERSIONA. RANGE – difference between the highest and lowest scores plus 1
  • 16. MEASURES OF SPREAD ORDISPERSIONB. VARIANCE – average of the squared deviations from the MEAN Computing for VARIANCE1. Get the deviation score for each score by subtracting it from the mean2. Square each resulting deviation3. Get the sum of all squared deviations4. Divide the result by the number of subjects for the population (N) or the number of subjects minus 1 (n-1) for a sample
  • 17. VARIANCE : formula For POPULATION For SAMPLE
  • 18. MEASURES OF SPREAD ORDISPERSIONC. STANDARD DEVIATION - indicates how much scores are spread around the mean - Square root of the variance
  • 19. Ex. Scores of 2 groups of students: Grp 1 : 46 60 65 65 70 80 90 Grp 2 : 62 66 68 70 70 70 70 GROUP 1 GROUP 2 MEAN 68 68 MEDIAN 65 70 MODE 65 70 RANGE 90-46+1 = 45 70-62+1 = 9 S.D. 14.3 3.06
  • 20. Variances and standard deviation in the sample distribution of scores SCORES Deviation of score from X Square of the Group 1 deviation 46 46 – 68 = 22 484 60 60 – 68 = 8 64 65 65 – 68 = 3 9 65 65 – 68 = 3 9 70 70 – 68 = 2 4 80 80 – 68 = 12 144 90 90 – 68 = 22 484 VARIANCE = sum of squared deviations n-1 = 484 + 64 + 9 + 9 + 4 + 144 + 484 = 199.67 7–1 STD. DEVIATION = square root of variance = = 14.3
  • 21. TESTS- Also used to make inferences- PARAMETRIC tests - for interval and ratio variables assuming that: sample was drawn from a normally distributed population if two groups are analyzed they have the same variance
  • 22. TESTS COMPARING GROUPS1. Tests to determine the difference between TWO groups2. Tests to determine the difference among THREE or more groups
  • 23. TESTS COMPARING GROUPS1. Tests to determine the difference between TWO groups a. T-test for independent groups b. T-test for paired data
  • 24. TESTS COMPARING GROUPS1. Tests to determine the differencebetween TWO groups a. T-test for independent groups - detects statistically significant differences between means - for static group comparison or randomized control group design (compare scores of 2 unmatched groups)
  • 25. TESTS COMPARING GROUPS a. T-test for independent groups ex. 2 groups of slow learners Oral • Mean post-instruction instruction group scores T test for more independent effective? • Mean post- groupsVideotape instruction group scores Dominguez (1985) “ A comparative study of the achievement of slow learners taught by oral tutorials with those taught by self-instructional programmed videotapes.”
  • 26. TESTS COMPARING GROUPS1. Tests to determine the differencebetween TWO groups b. T-test for paired data - identify statistically significant changes in a single group - or between matched groups
  • 27. TESTS COMPARING GROUPS2. Tests to determine the differenceamong THREE or more groups a. Univariate analysis of variance (ANOVA) b. Analysis of Covariance (ANCOVA) c. Multivariate analysis of variance (MANOVA)
  • 28. a. ANOVA Univariate analysis of variance- to determine significant difference among 3 or more group means (1 variable)Ex. Posttest scores of students to compareeffectiveness of 3 instructional strategies
  • 29. ANOVA Univariate analysis of variance Written Written + matl’s videotape n=47 n= 46 N= 168 2nd yr med students 23 2nd yr physician Written + assistant students small group Written+ practice video+ SGP n = 43 n = 55Students’ knowledge and skills were assessed after instruction todetermine any significant difference among the groups throughONE-WAY ANOVA “Teaching a screening musculoskeletal examination: A randomized control trial of different instructional methods.” Lawry et al. (1999)
  • 30. ANOVA Univariate analysis of variance One-way ANOVA Two-way ANOVA 4 X 2 ANOVA Three-way ANOVA
  • 31. b. ANCOVA Analysis of Covariance- Used to control differences among groups that existed before the study- Usually used in quasi-experimental designs- Ex. When Pre-test means of groups are significantly different from each other ANCOVA can be used to adjust pretest scores so they can be treated as identical
  • 32. c. MANOVA Multivariate analysis of variance- Groups are compared with respect to 2 or more dependent variables
  • 33. TESTS TO DETERMINE THE RELATIONSHIPAMONG VARIABLES IN A GROUP1. Pearson product moment correlation coefficient (interval / ratio variables)2. Regression
  • 34. TESTS TO DETERMINE THE RELATIONSHIPAMONG VARIABLES IN A GROUP1. Pearson product moment correlation coefficient (interval / ratio variables) - when there are 2 scores per subject - study intends to determine how these scores are relatedEx. Survey of pharmacists to determine work patterns and whether other factors (age, gender, # years in work force) affected the work patterns (Knapp et al. 1992)
  • 35. TESTS TO DETERMINE THE RELATIONSHIPAMONG VARIABLES IN A GROUP1. Pearson product moment correlation coefficient (interval / ratio variables)2. Regression - Simple regression – predicting one variable from another variable - Multiple regression – predicting values of 1 variable on the basis of the values of 2 or more variables
  • 36. TESTS TO DETERMINE THE RELATIONSHIP AMONG VARIABLES IN A GROUP1. Pearson product moment correlation coefficient2. Regression Ex. Study to identify predictors of dental skill dev’t - whether commonly examined fine motor ability tests (steadiness tester, mirror trace test) and maturational tests (hand length, index finger length, wrist width ) were associated with early scaling and root-planning skills in 120 dental students (Wilson, Waldman and McDonald 1991)
  • 37. COMMOMLY USED PARAMETRIC TESTS USES APPROPRIATE TESTSDetermining the differencesamong groupsBetween 2 related or matched T-test for paired datagroupsBetween 2 independent T-test for independent groupsgroupsAmong 3 or more groups ANOVA (1 dependent variable) ANCOVA (1 dep variable; quasi-exptl design) MANOVA ( 2/> dep variables)Determining the relationship Pearson product moment correlationamong variables in a group coeficient Regression
  • 38. NONPARAMETRIC TESTS- nominal and ordinal variables- when underlying assumptions for parametric tests are not met- for small sample size
  • 39. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the differencebetween TWO groups (1) McNemar Change test (2) Wilcoxon matched-pairs signed- ranks test (3) Permutation test for paired replicates
  • 40. NONPARAMETRIC TESTS (4) Fischer exact test for 2 X 2 table (5) Chi-square test (X2 test) (6) Wilcoxon-Mann-Whitney test (7) Robust rank-order test (8) Kolmogorov-Smirnov two- sample test (9) Permutation test for two independent samples
  • 41. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(1) McNemar Change test- For 2 related/ matched nominal variable- (Ex. Responses of a group of students on which 2 types of instructional methods they prefer when asked before and after being exposed to such methods)- Observed frequencies of students’ preferred instructional methodPreferred instructional method Preferred instructional method Totalbefore exposure before exposure Method A Method BMethod AMethod BTotal
  • 42. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(2) Wilcoxon matched-pairs signed-ranks test- For 2 related samples; ordinal data- Determines the direction of differences within pairs or related samples and relative magnitude of those differences
  • 43. NONPARAMETRIC TESTS(2) Wilcoxon matched-pairs signed- rankstestEx. To determine whether there is a significant differencein perceptions of graduates on their degree ofpreparedness in various aspects of training during theirclinical fellowship and degree of importance in clinicalpractice of those same aspects. (Atienza 2001)Perceived degree of preparedness and importance of graduatesGraduates Perceived degree of Perceived degree of Difference preparedness importanceGraduates AGraduates Betc.
  • 44. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(3) Permutation test for pairedreplicates- one of most powerful tests for paired observation- variables on interval scale- small sample size
  • 45. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(4) Fischer exact test for 2 X 2 table- nominal or ordinal data- two independent samples- sample size in small (n< 2)- subjects fall in one of two classes Variable Group Combined- Number of students who passed and failed I II Pass Fail
  • 46. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(5) Chi-square test (X2 test)- nominal or ordinal data- to determine the difference between 2 independent groups ( n > 20 ; each of the expected frequencies is 5/> )- for examining the differences among 3/> groups and- for testing association between 2/> categorical variables
  • 47. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(5) Chi-square test (X2 test)Ex. Cross sectional survey of 545 doctors toexamine young physicians’ views onprofessional issues (professional regulation,multidisciplinary teamwork, priority setting,clinical autonomy, private practice)These variables were tested againstdemographic variables like sex.Specialty choice revealed marked sexbias
  • 48. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(6) Wilcoxon-Mann-Whitney test- One of most powerful tests for data in ordinal scale- alternative to t-test- Used to predict the difference between 2 independent samples from same population or from populations with the same/equal variances
  • 49. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(7) Robust rank-order test- Does not assume that the 2 independent samples come from the same population- Does not require equal variances for the 2 populations from which the sample was taken
  • 50. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(8) Kolmogorov-Smirnov two-sampletest- 2 independent samples drawn from the same population or populations with the same distributions- Powerful for small samples
  • 51. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSa. Tests to determine the difference between TWO groups(9) Permutation test for twoindependent samples- Powerful for testing the difference between the means of two independent sample when their sample sizes are small- Requires interval measurement- No special assumptions about the distributions of the populations
  • 52. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSb. Tests to determine the differencebetween THREE or more groups (1) Cochran Q test (2) Friedman two-way analysis of variance by ranks (3) Kruskal-Wallis one-way analysis of variance
  • 53. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSb. Tests to determine the difference between THREE or moregroups(1) Cochran Q test- Extension of McNemar test used for 2/> related samples (nominal variables)- Used to analyze responses to a test or questionnaire
  • 54. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSb. Tests to determine the difference between THREE or moregroups(2) Friedman two-way analysis ofvariance by ranks- For ordinal data- to test if a number of repeated measures or matched groups come from the same population or populations with the same median
  • 55. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSb. Tests to determine the difference between THREE or more groups(2) Friedman two-way analysis ofvariance by ranks Three groups of subjects in four conditions Group Conditions / Variables Variable A Variable A Variable A Variable A Group I Group II Group III
  • 56. NONPARAMETRIC TESTS1. TESTS TO COMPARE GROUPSb. Tests to determine the difference between THREE or moregroups(3) Kruskal-Wallis one-way analysisof variance- for testing 3 or more independent groups for ordinal data- Ex. Testing for significant differences of socioeconomic scores or attitudinal scores based on specified criteria of students from different regions in the country
  • 57. NONPARAMETRIC TESTS2. MEASURES OF ASSOCIATION(a) Pearson product moment correlationcoefficient (interval, ratio)(b) Phi coefficient (nominal)(c) Kappa coefficient of agreement(nominal)(d) Spearmen rank-order correlationcoefficient (ordinal)(e) Kendall coefficient (ordinal)(f) Gamma statistic (ordinal)
  • 58. USES LEVEL OF MEASUREMENT NOMINAL ORDINAL INTERVALDetermining thedifference amonggroupsBetween 2 related/ McNemar change Wilcoxon signed ranks Permutation test formatched groups test test paired replicatesBetween 2 Fischer exact test Wilcoxon-Mann- Permutation test forindependent groups for 2X2 table Whitney test 2 independent Chi-square test Robust rank order test samples Komogorov-Smirnov two-sample testAmong 3/> related Cochran Q test Friedman 2-waygroups analysis of variance by ranksAmong 3/> Chi-square test Kruskall-Wallis one-wayindependent groups analysis of varianceDetermining Cramer coefficient Spearman rank-order correlation coefficientassociation Phi coefficient Gamma statistic Kappa confidence of agreement
  • 59. THANK YOU!