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HSC 2801 > 2801 Final > Flashcards

2801 Final Flashcards

1
Q

Surveys

A

Are correlational research. Causality may be inferred. Some survey research makes predictions (predictor variables & criterion variables). Types include questionnaires, interviews, and self-reported diaries.

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2
Q

Designing Good Surveys

A
  1. Consider research question 2. Define Constructs 3. Review existing instruments 4. Write items for each construct 5. Get advice 6. Pilot test items 7. Analyze stats 8. Re-work items 9. Administer final survey
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3
Q

Defining Constructs

A

The most important stage in survey design.

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4
Q

Types of Questions

A

Demographic information, open-ended items (tend to be subjective, used more in interviews), close-ended items. Note: Interval > Ordinal data for demographics

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5
Q

Scales

A

Categorical (nominal), Continuous (interval / ratio), Ranked (ordinal), Scores can either be summative or cumulative.

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6
Q

Likert Scaling Model

A

Summative scaling method with ranked values –> anchors are susceptible to bias –> Even # scale has no neutral choice

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7
Q

Semantic Differential Scale Model

A

Summative model, measures feelings by scaling between 2 extremes. Only extreme anchors labeled.

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8
Q

Visual Analogue Scales

A

Summative, only extreme anchors labelled, line of fixed length used for scale. Commonly used for pain.

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9
Q

Guttman Scales

A

Cumulative or hierarchical. Often describe functional limitations of patients.

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10
Q

Order of question types (in a survey)

A

Non sensitive (interesting), demographic (non-interesting), sensitive info, end with easy questions

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11
Q

Delphi survey

A

A survey in which participants are health-care practicioners, or experts in the field –> Develop consensus around a specific issue –> Useful for establishing norms in clinical practice.

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12
Q

Emic approach

A

Qualitative, begins with indicators & tries to determine constructs that fit. Goal is to understand.

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13
Q

Etic approach

A

Quantitative, begins with formal constructs & tries to develop empirical indicators. Goal is to predict.

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14
Q

Variance Questions

A

Focus on differences & correlations. Focus on testing predetermined solutions (Hypothesis testing)

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15
Q

Process Questions

A

Focus on how things happen. Focus on understanding –> Hypothesis generating

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16
Q

Qualitative research

A

Concern is with discovery & description. Qualitative research in health studies SDoHaD

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17
Q

5 Qualitative research study types

A

Normative-Biographical study, Phenomenological Study, Ground Theory study, Ethnographic & Case study

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18
Q

Normative-Biographical Study

A

Researchers focus on the meaning an individual finds in his/her experience.

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19
Q

Phenomenological Study

A

Studies a phenomenon. Researchers focus on recall & recounting of marker events (key experiences that shape an indiv’s life)

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20
Q

Ground Theory Study

A

Focuses on finding relationships or various interpretations an indiv applies to his/her experiences –> Researchers develop constructs grounded in daily life experiences

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21
Q

Ethnographic Study

A

Focus on cultural patterns of behavior & meanings people use to organize & interpret experiences

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22
Q

Case Study

A

Analysis of a case to invoke broader interpretations of the meaning –> Structured: Problem, context, issues, lesson learned

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23
Q

4 Methods of Data Collection

A

Interviews, Observation, Content Analysis, Focus Groups

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24
Q

Interviews

A

One method of data collection: Structured (fixed questions) or unstructured (questions develop as interview progresses)

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25
Q

Observation

A

One method of data collection: Direct determination of “here and now” experiences. Either passive or participant.

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26
Q

Content Analysis

A

One method of data collection: In depth look at qualitative materials

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27
Q

Focus groups

A

One method of data collection: Investigators act as moderators, facilitating a discussion

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28
Q

Analysis Process

A

1) Data managing (data storage) 2) Reading / memoing (read, note, form initial ideas) 3) Describe data 4) Classifying 5) Interpreting 6) Representing & visualizing

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29
Q

Sampling

A

Evaluate saturation to determine sample efficiency –> When sufficient information exists to predict responses it is saturated. Saturation is difficult to quantify.

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30
Q

Tracy’s 8 “Big Tent” Criteria

A

1) Worthy Topic 2) Rich Rigor 3) Sincerity 4) Credibility 5) Resonance 6) Research provides Significant Contribution 7) Research is Ethical 8) Meaningful Coherence

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31
Q

Weakness of Qualitative research

A

Labor intensive, involves exp-based learning, lack of formal rigor in data collection & analysis, more difficult to establish credibility.

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32
Q

Variables

A

Independent vs dependent, predictor vs criterion, subject, continuous vs discrete

33
Q

Levels of measurement

A

1) Nominal –> Categorical data w/no implicit ordering, unequal distance between points. 2) Ordinal –> categorical w/implicit ordering, unequal distance between points. 3) Interval –> Continuous (eq dist between points) and no meaningful 0. 4) Ratio –> Continuous, meaningful zero.

34
Q

True Scores & Errors

A

Any deserved score (x) is comprised of 2 distinct components: (T) True score & (E) Error component. X = T + E. X - T = Measurement error.

35
Q

Measurement Error

A

1) Systematic errors –> Predictable, reliable, thus more of a validity concern. 2) Random –> Occur due to chance. As Re’s decrease, T approaches X, measure becomes more reliable.

36
Q

Validity

A

Construct validity (does measure represent what it’s supposed to), Internal Validity (are effects due slely to experimental conditions), External Validity (can results be applied to other settings or populations), Statistical conclusion validity (were appropriate methodological & statistical techniques applied)

37
Q

Utility

A

Is the data precise & reliable at lowest cost (efficiency), can the method be applied (generality)

38
Q

Measurement issues in the assessment of change

A

1) Lvl of measurement 2) Reliability 3) Stability 4) Linearity

39
Q

Measurement issues in Lvl of measurement

A

Nominal scores can’t be subtracted, ordinal scores have unequal distances between points, Interval scores can be subtracted but amount of change can’t be computed –> Change is best measures by ratio measures

40
Q

Measurement issues in Lvl of reliability

A

If measures are unreliable, your change score will contain mostly error. Suggested change scores only be used when reliability exceeds 0,5 but should exceed 0.7 in practical settings.

41
Q

Measurement issues in Lvl of Stability

A

Important in situations where there may be substantial variability in performance.

42
Q

Measurement issues in Lvl of Linearality

A

The shape of the relationship with time may effect measrements of change.

43
Q

Evaluating Diagnostic Procedures

A

Sensitivity = test’s ability to obtain a “true positive” can be calculated by true positives over total positives, Specificity = test’s ability to obtain “true negative” can be calculated by true negatives over total negatives

44
Q

Positive Predictive value (PV+)

A

Likelihood a person testing positive will have the disease Can be calculated by dividing true positives over total number of positive screens.

45
Q

Negative Predictive Value (PV-)

A

Likelihood a person testing negative will be disease free. Can be calculated by dividing true negatives over total number of negative screens.

46
Q

Cross-validation

A

Assessments of construct validity –> use a measure to predict group membership in a sample different from the one used to originally determine a cut off score

47
Q

Criterion Referenced Tests

A

Interpreted relative to a standard that represents an acceptable level of performance

48
Q

Norm-referenced tests

A

Interpreted relative to the performance of a peer group (established by testing a large group, and establishing cutoffs with the distribution)

49
Q

Mean

A

Interval & Ratio only, it’s the average of the data

50
Q

Median

A

Ordinal, interval or ratio, it’s the point that divides the data in half (n+1)/2

51
Q

Mode

A

Nominal, ordinal, interval, or ratio, it;s the most frequently occurring vlaue

52
Q

Central Tendency

A

If distribution is normal (Bell-shaped), mean, median & mode are all the same.

53
Q

Dispersion

A

Range (ID highest & lowest values) & Standard deviation (more accurate & detailed)(shows relation that indiv scores have to the mean sample)

54
Q

SD formula

A

draw

55
Q

SD (Computational formula)

A

draw

56
Q

Normal Distribution

A

“bell curve” –> Mean, median & mode is 0, standard deviation is 1. 68% w/1SD, 95% w/2SD, 99% w/3SD

57
Q

Standard scores

A

Easiest way to compare scores on a common scale is using z or T score

58
Q

Z-Score

A

Is a standard measure of the distance between a single point in the data & the overall mean for that variable.

59
Q

Z-score formula

A

Draw

60
Q

Z-distribution

A

ranges from - infinity to + infinity, mean of 0 and STD of 1

61
Q

T-score

A

Is a standard distribution with a mean of 50 & std of 10 and no negative values.

62
Q

T-score formula

A

T = 10z + 50

63
Q

Histogram

A

Compares multiple measurements of the same variable

64
Q

Bar graph

A

Compares multiple variables

65
Q

Stem & leaf

A

A vertical histogram, shows raw data & gives a rough idea of dispersion.

66
Q

Frequency polygen

A

Similar to a histogram, useful in summarizing interval-level data

67
Q

Line graphs

A

Often used to convey temporal information

68
Q

Box Plots

A

IQR = QU - QL, outliers fall outside boundary set by median (+/- 1.5 x IQR), extreme outliers fall outside boundary set by median +/- 3.0 x IQR)

69
Q

5 Number summary (Box plots)

A

Min value, lower quartile, median, upper quartile, max value

70
Q

Notched box plots

A

Like box plot but adds 95% CI. Median > mean = negative skew, median < mean = positive skew. IQR = middle 50% of data.

71
Q

Hypothesis

A

Is our best guess. Research / alternate hypothesis (best guess) vs Null hypothesis (nothing happened). We always test against null hypothesis. Can never accept null hypothesis only able to reject or fail to reject.

72
Q

Directional Hypothesis

A

A specific result being tested in a direction from the mean (upper tail or lower tail test)

73
Q

Non-directional hypothesis

A

One is comparing change that may occur in either tail of the distribution (two-tailed) –> 2 critical values & therefore two rejection areas for null hypothesis

74
Q

Alpha

A

Probability of incorrectly concluding that there is an effect (T1 Error)

75
Q

Power

A

Ability to determine true relationships that exist within the data.

76
Q

Determining power

A

Power is equal to 1 - beta. Where beta is a type 2 error.

77
Q

T2 Error

A

When Ho is false but you fail to reject Ho.

78
Q

Estimating Alpha

A

Know Z-score formula, apply to Z table and subtract z from 1 to determine alpha

79
Q

Central limit theorem

A

As sample size increases, approximation of normality in the sampling distribution improves, aka the normal convergence theorem.

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