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Data Gathering and Data analysis in Qualitatiive Nursing Research
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- 3. Who will collectthe data? ▹ Principal researcher ▹ Other people outside the research team ▹ data collectors are paid for their services 3
- 4. 4 When will thedata 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 thedata 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
- 6. 6 What data willbe collected? ▹ This question calls for a decision to be made about the type of data being sought. ▹ For example, if the researcher is concerned with the way crises affect people “what” > persons’ behaviors or responses in crises
- 7. 7 How will thedata be collected? ▹ Research instrument ￭ self-report questionnaire ￭ Sophisticated physiological instruments ￭ Observation ￭ Interviews ￭ Scales
- 8. 8 Data Collection Instruments ▹ Researchinstruments – facilitates the observation and measurement of the variables of interest ▹ Example: ￭ physiological data- physiological instrument ￭ Observational data- observational checklist
- 9. 9 Use of existinginstruments ▹ 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 ▹ pretesta 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 ▹ Browne10 cites a general flat rule to
- 12. 12 Reference • Birkett andDay (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 degreeto 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 ▹ refersto 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 ▹ definedas 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 ▹ concernedwith 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 ▹ theinstrument 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 ▹ amethod 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-onecontact ▹ 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
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- 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 ￭ Measurementsuses 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 ￭ Aremeasured, 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 0 -Number of family members -Weight -Length -Distance -Number of books
- 31. 31 Important things toconsider in choosing a particular analysis · The type of data set Þ Discrete Data (counts, ranks) Non-Parametric Tests Þ Continuous Data (ratio, interval) Parametric Tests
- 32. 32 Statistical Analysis ▹ Determinewhat 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 ▹ Tosummarize data set: ￭ Frequency Tables ￭ Central Tendencies ⬝ Mean ⬝ Median ⬝ Mode
- 34. 34 Descriptive Statistics ▹ Todetermine 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 Tomake comparisons or determine relationships between variables: Inferential Statistics
- 36. 36 Inferential Statistics · Significantrelationships are determined by rejecting the null hypothesis and accepting the alternative hypothesis Þ Ho: Variable A = Variable B Þ H1: Variable A = Variable B
- 37. 37 Probability ▹ Probability isthe 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 ofthe 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 ▹ Researchhypothesis: 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 weneed 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 ▹ Tocompare 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 ▹ Todetermine the relationship between two variables: Þ Counts or Continuous Data Pearson Product Moment Correlation (r) Scatter plot Þ 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 ▹ Toextrapolate 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 Þ 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 ▹ Todetermine significant differences: 1. 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-ParametricTests (Discrete Data): Wilcoxon Rank Sum Test: Independent population Wilcoxon Signed Rank Test: Dependent Population, Mean vs Standard
- 49. 49 Inferential Statistics 2. ToCompare > 2 variables: ANOVA (Analysis of Variance) One-Way ANOVA used to compare the means of more than two samples (M1, M2…MG) in a between- subjects design
- 50. 50 ANOVA Example: ▹ compare thecalorie 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 ofANOVA is significant: ▹ Post hoc test: ￭ Student Neuman Keuls Test ￭ Duncan’s Multiple Range Test ￭ Tukey’s (parametric test)
- 52. 52 Statistical Analysis ▹ Todetermine 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
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Editor's Notes
- #2 There are five important questions to ask when the researcher is in the process of collecting data: Who? When? Where? What? How?
- #3 scientific investigations frequently involve a team of researchers Anytime more than one person is involved, assurances must be made that the data are being gathered in the same manner. Training will be needed for the data collectors, and checks should be made on the reliability of the collected data.
- #4 Duration – the only way to answer this question is through a trial run of the procedure by the researcher The decision may be made to revise the instrument if it seems to take too long for completion.
- #5 Optimum conditions - Having participants fill out questionnaires in the middle of the hallway while leaning against a wall would definitely not provide the optimum setting. While researcher is present – helps ensure return of the questionnaires At leisure time - answers may be more accurate but reduction in the return rate of the questionnaires
- #7 Choosing a data-collection instrument is a major decision that should be made only after careful consideration of the possible alternatives.
- #9 While conducting a review of the literature on the topic of interest, a researcher may discover that an instrument is already available to measure the research variable(s). Many of the existing instruments are copyrighted. The copyright holder must be contacted to obtain permission to use such an instrument. Sometimes this permission is given without cost, and other times the researcher has to pay for permission to use the instrument or purchase copies of the tool. Frequently, the only request that will be made is that a copy of the study results and the data, particularly data on the reliability and validity of the instrument, be forwarded to the person who developed the instrument.
- #10 Caution must be exercised when this approach to instrument development is used. If any items are altered or deleted, or new items added to an existing instrument, the reliability and validity of the tool might be altered.
- #14 Test-retest - The assumption is that for traits that have not changed, any differences in people’s scores on the two testing are the result of measurement error. When score differences across waves are small, reliability is high.
- #15 when observers or raters are used in a study, the percentage or rate of agreement may also be used to determine the reliability of their observations or ratings Internal consistency- In responding to a self-report item, people are influenced not only by the underlying construct but also by idiosyncratic reactions to the words. By combining multiple items with various wordings, item irrelevancies are expected to cancel each other out. An instrument is said to be internally consistent to the extent that its items measure the same trait. If an instrument is supposed to measure depression, for example, all of the items on the instrument must consistently measure depression.
- #18 Although face validity is not considered good evidence of validity, it is helpful for a measure to have face validity if other types of validity have also been demonstrated.
- #19 are the number and type of items adequate to measure the concept or construct of interest? These experts are given copies of the instrument and the purpose and objectives of the study. They then evaluate the instrument, usually individually rather than in a group. Comparisons are made between these evaluations, and the researcher then determines if additions, deletions, or other changes need to be made.
- #30 NOMINAL simplest, lowest, most primitive type involves classification of events into categories that must be distinct, one-dimensional, mutually exclusive and exhaustive; and the resulting scales are “naming” scales Characteristics: It involves nominal categories & is essentially a qualitative and a non-mathematical measurement It names and classifies data into categories It doesn’t have a zero point It cannot be ordered in a continuum of low-high It produces nominal or categorical data It assumes no equal units of measurement It assumes the principle of equivalence ORDINAL involves not only categorizing elements into groups but also ordering of data and ranking of variables in a continuum ranging according to magnitude, that is, from the lowest to the highest point Characteristic: It refers to ranks based on a clear order of magnitude of low and high signifying that some elements have more value than others The numbers have actual mathematical meaning as well as having identification properties It is essentially a quantitative measurement It shows a relative order of magnitude INTERVAL Provides information about the distance between the values, and contains equal intervals, ordering subjects into one of them Characteristic: It includes equal units It is essentially quantitative measurement It specifies the numerical distance between the categories It does not have a true zero point RATIO includes the other three forms offer, plus the option of an absolute true zero as its lowest value, which in essence indicates absence of the variable in question. Allows the researcher to make statements about proportions and ratios, that is, to relate one value to stimulus
- #44 What is the relationship between year in college (freshman, sophomore, junior, senior) and use of campus counseling services? Responses to this question will involve a count of how many in each group use the counseling service
- #47 Parametric test used when the researcher can assume that the population values are normally distributed, variances are equal Two (or more) samples are called independent if the members chosen for one sample do not determine which individuals are chosen for a second sample. Two (or more) samples are called dependent if the members chosen for one sample automatically determine which members are to be included in the second sample.
- #51 When we reject the null hypothesis in a one-way ANOVA, we conclude that the group means are not all the same in the population. But this can indicate different things. With three groups, it can indicate that all three means are significantly different from each other. Or it can indicate that one of the means is significantly different from the other two, but the other two are not significantly different from each other. statistically significant one-way ANOVA results are typically followed up with a series of post hoc comparisons of selected pairs of group means to determine which are different from which others