Data Gathering and Data analysis in Qualitatiive Nursing Research

Data Gathering

1. Data Collection

2. Who? When? Where? What? How? 2

3. Who will collect the data? Principal researcher Other people outside the research team data collectors are paid for their services 3

4. 4 When will the data be collected? Determine the month, day, and sometimes even the hour for data collection Include how long the data collection will take If questionnaires will be used, they should be pretested with people similar to the potential research participants , to determine the length of time for completion of the instrument.

5. 5 Where will the data be collected? Optimum conditions should be sought . If the participants happen to be tired or the room is too hot or too cold, the answers that are provided may not be valid . If questionnaires are being used respondents can complete the questionnaire while the researcher remains in the same immediate or general area Respondents can complete the questionnaires at leisure

6. 6 What data will be collected? This question calls for a decision to be made about the type of data being sought. For example, i f the researcher is concerned with the way crises affect people “ what ” > persons’ behaviors or responses in crises

7. 7 How will the data be collected? Research instrument self-report questionnaire Sophisticated physiological instruments Observation Interviews Scales

8. 8 Data Collection Instruments Research instruments – facilitates the observation and measurement of the variables of interest Example: physiological data- physiological instrument Observational data- observational checklist

9. 9 Use of existing instruments helps connect the present study with the existing body of knowledge on the variables Advantage: Discussion on the validity and reliability of the tool is available Tested and widely used by previous studies

10. 10 Developing an instrument Done when no existing instrument can be discovered as appropriate for the study Revision of existing instrument is also possible New reliability and validity testing will need to be conducted permission to revise the instrument will have to be obtained from the developer of the tool

11. 11 Pilot studies pretest a newly designed instrument a small-scale trial run of the actual research project A group of individuals similar to the proposed study subjects should be tested in conditions similar to those that will be used in the actual study Assess: readability, accuracy and comprehensibility Browne 10  cites a general flat rule to 'use at least 30 subjects or greater to estimate a parameter'

12. 12 Reference Birkett and Day (1994 ) Browne (1995 ) Kieser and Wassmer (1996 ) Julious (2005 ) Sim and Lewis (2011) Teare , et al. (2014) Comments Suggested 20 for Internal pilot studies . Mentions that the use of 30 is commonplace at the time Use when main trials are between 80 and 250 and using UCL. Recommended minimum of 12 subjects per group. Use for small to medium effect sizes to minimize combined size . Based on an extensive simulation study. Recommended Pilot Study Sample size 20 30 20 to 40 24 ≥55 ≥70

13. 13 Practicality of the Instrument The practicality of an instrument concerns its cost and appropriateness for the study population . How much will the instrument cost? How long will it take to administer the instrument? Will the population have the physical and mental stamina to complete the instrument? Are special motor skills or language abilities required of participants? Does the researcher require special training to administer or score the instrument?

14. 14 Reliability of the Instrument The reliability of an instrument concerns its consistency and stability . In test–retest reliability , replication takes the form of administering a measure to the same people on two occasions (e.g., 1 week apart).

15. 15 Reliability of the Instrument an interrater (or inter-observer ) reliability assessment involves having two or more observers independently applying the measure with the same people to see if the scores are consistent across raters internal consistency- captures consistency across items

16. 16 Reliability of the Instrument reliability yield coefficients that summarize how reliable a measure is The reliability coefficients normally range in value from 0.0 to 1.0, with higher values being especially desirable . . 80 or higher are considered desirable

17. 17 Validity the degree to which an instrument is measuring the construct it purports to measure Example: When researchers develop a scale to measure resilience , they need to be sure that the resulting scores validly reflect this construct and not something else, such as self-efficacy or perseverance.

18. 18 Face Validity refers to whether the instrument looks like it is measuring the target construct The face validity of an instrument can be examined through the use of experts in the content area, or through the use of individuals who have characteristics similar to those of the potential research participants.

19. 19 Content Validity defined as the extent to which an instrument’s content adequately captures the construct Content validity is usually assessed by having a panel of experts rate the scale items for relevance to the construct and comment on the need for additional items

20. 20 Construct Validity concerned with the degree to which an instrument measures the construct it is supposed to measure 2 methods: Known-groups procedure Factor analysis

21. 21 Known-groups procedure the instrument under consideration is administered to two groups of people whose responses are expected to differ on the variable of interest Example: tool measuring depression a group of supposedly depressed subjects a group of supposedly happy subjects Outcome: you would expect the two groups to score quite differently on the tool

22. 22 Factor analysis a method used to identify clusters of related items on an instrument or scale helps the researcher determine whether the tool is measuring only one construct or several constructs Correlational procedures are used to determine if items cluster together.

23. 23 Distribution of Questionnaires one-to-one contact Questionnaires also may be placed in a container in a given location where potential respondents can take one if they so desire Using online software program like google forms or SurveyMonkey

24. 24 Data collection methods Interviews Observation Attitude Scales Physiological and Psychological Measures

25. Data Analysis

26. What is statistics is a range of procedures for gathering , organizing , analyzing and presenting quantitative data

27. 27 Objectives of Statistics Descriptive To summarize and describe sets of observations Inferential To make an inference (determine significant differences, relationships between sets of observations ) Exploratory Artificial classification of sets of observations

28. 28 Variables Discrete Measurements uses whole units or numbers with no possible values between adjacent units Counted not measured E.g. Family size: 2, 4, 5

29. 29 Variables Continuous Are measured, not counted Measurements uses smaller increments of units E.g. height, temperature, distance, age The type of data set is one of the determinants in choosing the appropriate analysis

30. 30 Levels of Measurement Nominal - Male / female -Black / white -Young / old -Single / married / widowed -Nationality -Type of shoes -Skin color -Type of music Ordinal - Status (low, middle, high) -Size (smallest, small, big, biggest) -Quality (poor, good, very good, excellent) Interval - Degrees of temperature - Calendar time -Attitude scales - IQ scores Ratio - Interval level with - Number of family members - Weight - Length - Distance - Number of books

31. 31 Important things to consider in choosing a particular analysis The type of data set Discrete Data (counts, ranks) Non-Parametric Tests Continuous Data (ratio, interval) Parametric Tests

32. 32 Statistical Analysis Determine what needs to be done based on the problem or specific objective of the study To summarize or describe data: Descriptive Statistics

33. 33 Descriptive Statistics To summarize data set: Frequency Tables Central Tendencies Mean Median Mode

34. 34 Descriptive Statistics To determine dispersion or variation of data: Range (minimum and maximum values Standard Deviation (measure of precision: “how close are your measurements ”) Confidence Interval (measure of accuracy: “how close are you to the true value”)

35. 35 Statistical Analysis To make comparisons or determine relationships between variables: Inferential Statistics

36. 36 Inferential Statistics Significant relationships are determined by rejecting the null hypothesis and accepting the alternative hypothesis H o : Variable A = Variable B H 1 : Variable A = Variable B

37. 37 Probability Probability is the scientific way of stating the degree of confidence we have in predicting something Tossing coins and rolling dice are examples of probability experiments

38. 38 The role of the Normal Distribution If you were to take samples repeatedly from the same population, it is likely that, when all the means are put together, their distribution will resemble the normal curve . The resulting normal distribution will have its own mean and standard deviation. This distribution is called the sampling distribution and the corresponding standard deviation is known as the standard error .

39. 39 Hypotheses Revisited Research hypothesis: the research prediction that is tested (e.g. students in situation A will perform better than students in situation B) Null hypothesis: a statement of “no difference” between the means of two populations (there will be no difference in the performance of students in situations A and B)

40. 40 Why do we need a Null Hypothesis? The null hypothesis is a technical necessity of inferential statistics The research hypothesis is more important than the null hypothesis when conceiving and designing research

41. 41 Levels of Significance Used to indicate the chance that we are wrong in rejecting the null hypothesis Also called the level of probability or p level p =.01, for example, means that the probability of finding the stated difference as a result of chance is only 1 in 100

42. 42 Errors in Hypothesis Testing A type I error is made when a researcher rejects the null hypothesis when it is true The probability of making this type of error is equal to the level of significance A type II error is made when a researcher accepts the null hypothesis when it is false As the level of significance increases, the likelihood of making a Type II error decreases

43. 43 Interpreting Levels of Significance Researchers generally look for levels of significance equal to or less than . 05 If the desired level of significance is achieved, the null hypothesis is rejected and we say that there is a statistically significant difference in the means

44. 44 Inferential Statistics To compare Frequency Tables: Chi-Square Test Frequency Tables vs Theoretical Distribution: Goodness of Fit Test Comparing 2 or more Frequency Tables: Contingency Table Test

45. 45 Inferential Statistics To determine the relationship between two variables: Counts or Continuous Data Pearson Product Moment Correlation (r) Scatter plot Rank Data Set Spearman Rank Correlation (r) If r approaches 1 : the relationship is directly proportional If r approaches 0 : there is no relationship If r approaches -1: the relationship is inversely proportional

46. 46 Inferential Statistics To extrapolate values (given x what is y?): Regression Analysis For a simple linear regression (y = a + bX ), the analysis will determine the a and b values in the equation In principle, the regression analysis can only predict values with the range of the values of the samples used in the correlation.

47. 47 Inferential Analysis To determine significant differences: Compare 2 variables: Parametric Test (Continuous data or Discrete Data with N>40) t-test T-test: Mean vs Standard Pooled estimate of variance t-test (independent population) Paired t-test (Dependent Population)

48. 48 Inferential Statistics Non-Parametric Tests (Discrete Data): Wilcoxon Rank Sum Test: Independent population Wilcoxon Signed Rank Test: Dependent Population, Mean vs Standard

49. 49 Inferential Statistics 2. To Compare > 2 variables: ANOVA (Analysis of Variance) One-Way ANOVA used to compare the means of more than two samples ( M1 ,  M 2 … M G ) in a between-subjects design

50. 50 ANOVA Example: compare the calorie estimates of psychology majors, nutrition majors, and professional dieticians The means are 187.50 ( SD  = 23.14), 195.00 ( SD  = 27.77), and 238.13 ( SD  = 22.35), respectively p  value is . 0009 = reject null hypothesis

51. 51 If result of ANOVA is significant: Post hoc test: Student Neuman Keuls Test Duncan’s Multiple Range Test Tukey’s (parametric test)

52. 52 Statistical Analysis To determine patterns or artificial groupings among the variables: Exploratory Statistics Cluster Analysis develops artificial groupings based on an index of dissimilarity generated from the occurrence or weight of attributes in the variables being studied

53. THANKS! Any questions? 53