Introduction to Research Exam 2

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Introduction to Research Exam 2
2015-11-01 19:33:28

Exam 2 Intro to Research Pitt
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  1. Primary objective of SSD
    investigate the cause and effect relationship between the intervention and the effect on outcomes and identify patterns of change
  2. IV of and SSD
  3. DV of SSD
    outcomes (also termed target behavior which is observable, quantifiable, and a valid indicators of treatment effectiveness)
  4. Sample size of SSD
    1-10 subjects
  5. Phase A
    baseline (prior to intervention) or withdrawal (after intervention)
  6. Phase B
  7. Phases C-Z
    other interventions
  8. Length of phases
    consider type of patient, treatment, and expected rate of change in target behavior, use relatively equal lengths and extend the baseline until stability is achieved or true nature of the condition is presented
  9. Requirement for repeated measures
    must have consistency and reliability in measuring tools/techniques
  10. Pattern of Reversal (withdrawal) design
    ABA--intervention is removed after the intervention phase
  11. Internal validity of reversal design
    • has strong internal validity compared to regular AB designs 
    • It demonstrates the causal relationship between effect and intervention
  12. Limitations of reversal design
    • ethical concerns with withdrawing treatment--if treatment is effective and you take it away 
    • subjects may also be unhappy about reversal and perform worse
  13. When can reversal designs not be used?
    • they cannot be applied to studies where the IV has strong long-term effects (ex. learning something--can't unlearn it)
    • baseline will not be able to be attained again
  14. When should multiple baseline designs be used
    reversal designs are not feasible due to ethical concerns or the changes by the intervention cannot be reversed
  15. Internal validity of multiple baseline designs
    to show that changes occur only when the intervention is introduced no matter when intervention began
  16. Requirements of multiple baseline designs
    • repeated measurement of 3 or more data series (can be across subjects, across settings, or across behaviors)
    • all baselines must exhibit sufficient stability
    • the intervention must be applied to only the first series while other baselines are continued
    • first series achieve stability treatment 
    • intervention introduced to the second series
  17. Limitations fo multiple baseline designs
    causal relationships may not be clearly demonstrated, implementation of the study can be time consuming
  18. Multiple Baseline Designs should NOT be used when
    there is only one subject in one setting and one target behavior (DV)
  19. Essential feature of Alternating treatment design
    rapid alteration of two or more interventions or treatment conditions and the capability to study multiple treatments
  20. Internal validity of alternating treatment design
    randomize the order of different treatments can enhance internal validity (advantage)
  21. When to use an alternating treatment design
    • treatment effects are immediate
    • outcome is a clear consequence of one specific treatment
    • outcome is capable of changing quickly
    • treatments must be able to trigger those quick changes
  22. When to NOT use an alternating treatment design
    when the outcome takes time to change
  23. Results of a single subject study do not involve
    any traditional statistical methods
  24. How are results from SSDs analyzed?
    via interpretation of graphs (comparisons within each phase and between different phases)
  25. Level (method of visual inspection)
    mean DV in each phase (increases or decrease and amount of change based on lines)
  26. Trend (method of visual inspection)
    shows the rate of change in behaviors (direction pos or neg, speed, and patterns of changes)
  27. Stability or Variability (method of visual inspection)
    stability reflects the consistency of response over time
  28. Timing (method of visual inspection)
    observing how quickly the line changes
  29. Slope (method of visual inspection)
    shows rate at which the line changes and can also show a positive or negative relationship
  30. Advantages of SSD
    • observe change at an individual level
    • streamline with clinical practice
    • quick to know results and easy for clinicians to interpret them 
    • relatively inexpensive
    • provide control for most threats to internal validity (same subject repeatedly measures and patterns can be compared)
  31. Disadvantages of SSD
    • lacks external validity--inability to generalize results to other individuals and to other situations/settings
    • lack well-defined principles to apply systemic statistical analysis, just visual inspection (can be subjective)
  32. When to use SSD
    • when target population is small
    • when you want to focus on patterns of changes in DV of individual subjects induced by the intervention
    • conduct the study along with clinical practice/in a clinical setting
  33. Descriptive Statistics
    used to transform data into an interpretable form and summarize a set of data from your sample
  34. frequency distribution
    allows us to begin to analyze the data by looking at the number of time each value occurred or its frequency (often expressed as percentages of the total distribution)
  35. Null hypothesis
    generally a statement that assumes that there is NO difference between two sets of values, which is the group means are not different
  36. Alternative hypothesis
    there IS a difference between the two sets of values
  37. The purpose of an experiement
    to REJECT (disprove) the null hypothesis in the target population, based on the data collected from the sample
  38. P value <0.05
    • there is a significant difference between the expected and observed 
    • REJECT the null hypothesis
  39. P value >0.05
    • there is NO significant difference between the expected and observed
    • FAIL to reject the null hypothesis
  40. Does the p value indicate magnitude
    • NO!!!!! it is just an arbitrary and does NOT tell magnitude or guarantee clinical importance 
    • Ex. 0.01 not more significant than 0.05
  41. Type 1 Error
    • rejecting the null hypothesis when it is actually true 
    • incorrect rejection of a true null hypothesis
    • level of significance (alpha) usually=0.05
    • ex. think that an effect or relationship occurs when it doesn't
  42. Type 2 error
    • the null hypothesis is not rejected when it is false 
    • failure to reject a false null hypothesis
    • denoted as beta= 1-power and the power is normally 0.8
  43. Effect size
    a standardized and unites value that can be used to compare intervention effect on different outcome measures
  44. Does effect size tell the magnitude?
    YES! it tells the magnitude of treatment effect and helps to interpret clinical significance of results
  45. A p value of <0.05 tells you
    if the study is GENERALIZABLE to the population your sample represents
  46. Interpret Cohen's D for continuous outcome
    • 0.2=small effect
    • 0.5=medium effect
    • 0.8=large effect
  47. Dichotomous Outcome
    normal variables are normal variables which have only two categories or levels
  48. Number needed to treat
    offers a measurement of the impact a medicine or therapy by estimating the number of patients that need to be treated in order to have a beneficial impact on one person
  49. Confidence interval
    a range of scores with specific boundaries, that should contain the population mean
  50. 95% CI
    range of values that you can be 95% certain the true mean of population
  51. If you increase the sample size
    the width of the CI will be smaller and you decrease your margin of error
  52. if you increase your confidence level to 99%
    the width of the CI increases and you increase your margin of error
  53. center line of forest plot
    • indicates not difference between groups
    • if CI line passes it then NOT significant
  54. horizontal line of forest plot
    95% confidence interval
  55. Diamond of forest plot
    • compiled or pooled estimate from all the studies
    • location shows overall significance
  56. square size
    • weight of the study (related to sample size and quality)
    • weight does not influence significance
  57. I^2
    • the percentage of observed total variation across studies that is due to real heterogeneity rather than chance (ranges from 0-100%)
    • Should NOT be larger than 75%
    • do not recommend to combine studies with heterogeneity
  58. Power analysis--4 variables
    power, level of significance, effect size, and sample size
  59. t test
    used to compare two means
  60. independent (unpaired) t test
    • used when two independent groups of subjects are compared 
    • independent groups must have independent sets of subjects with no inherent relationship derived
  61. paired t test
    used when each subject is exposed to both interventional conditions and subjects used as their own controls
  62. One tailed test
    • if we are only interested in one side of the probability distribution 
    • we are testing whether one mean is higher than the other
  63. two tailed test
    if we are interested to see whether two means are different from each other
  64. Inappropriate use of multiple t tests
    each time you conduct a t test you increase the amount of type 1 error present (usually 5%)
  65. If you want to compare the effects of three different treatments you should use
    ANOVA (NOT t test!!)
  66. ANOVA
    analysis of variance is a tool used for analyzing multilevel and multi factor experimental designs (used to compare 3 means)
  67. ANOVA H0
    means all of the groups/conditions are equal (allows for 3 or more groups/conditions)
  68. ANOVA Ha
    • not all the meals are equal
    • DOESNT say how or which ones differ 
    • (need post-hoc for that
  69. T or F: excel can run a post-hoc test to tell which ones are different
    FALSE! excel can only run the ANOVA test that can tell you whether all the means are equal
  70. Correlations
    • a measure of association between two numerical variables
    • used to describe the strength and direction of a relationship between two variables
    • correlation≠causation
    • always a number from -1 to 1
  71. 0.1-0.3=
    • weak
    • moderate
    • strong
  72. Calculate r if the data is not linear??
  73. Regression
    • used when researcher wants to use the known relationship to predict an outcome 
    • useful for a prognosis
    • regression≠causation
  74. Independent variable
    • predictive variable (X)
    • variable used to predict another variable
  75. Dependent variable
    • criterion variable (Y)
    • variable that the analysis predicts
  76. Regression line
    best fit line using least square method excluding all outliers
  77. residuals
    • the distance between the y values and their corresponding estimated values on the line
    • include other factors that effect Y but are not included in the regression model
  78. Y=B0+B1X +residual
    • B0 is the regression constant and the Y int (DV) representing the value of Y when X=0
    • B1 is the regression coefficient which is the slope of the line
    • Y is the predicted value of Y
    • X is the IV on the x axis
  79. R^2
    • coefficient of determination (square of correlation coefficient)
    • means how much percentage that the X can account for the variation of Y
  80. P value <0.05 in regression model
    means regression model overall predicts outcome Y significantly well