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Chapter 12: Single-Subject Designs

Chapter 12: Single-Subject Designs. An alternative to experimental designs Purpose: To draw conclusions about the effects of treatment based on the responses of a single patient under controlled conditions. Based on:

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Chapter 12: Single-Subject Designs

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  1. Chapter 12: Single-Subject Designs • An alternative to experimental designs Purpose: • To draw conclusions about the effects of treatment based on the responses of a single patient under controlled conditions. • Based on: • A research hypothesisindicating expected relationship between independent and dependent variables • Specific operational definitions

  2. Single-Subject Designs • Independent Variable- The intervention • Dependent Variable- • The patient response (defined as the target behavior) • Target behavior is observable, quantifiable, and a valid indicator of treatment effectiveness

  3. Single-Subject Designs • Can be used to study comparisons between: • Several treatments • Components of treatments • Treatment and no-treatment conditions

  4. Structure of Single-Subject Designs • Repeated Measurement • Systematic collection of repeated measurements of a behavioral response over time • These repeated assessment are required to observe trends or patterns and evaluate variability of the behavioral responses over time

  5. Design Phases • Delineation of at least two testing periods: • Baseline phase • Intervention phase • Target behavior is measured across both phases

  6. Design Phases • Baseline information: • Responses of target behavior during a period of “no treatment” • Reflects the target behavior over time in the absence of the independent variable (intervention) • Changes from baseline to the intervention phase are attributed to the intervention

  7. Design Phases • Design phases are plotted on a line graph • Magnitude of the target behavior along the Y-axis • Time (sessions, trial, days, weeks) along the X-axis • Baseline is represented by the letter A • Intervention by the letter B

  8. Design Phases • The design of one baseline period followed by one intervention period is: A- B design • Baseline data collection • Unique to Single-Subject Design • (all other designs treatment is initiated following assessment)

  9. Baseline Data Collection • Traditional designs make it impossible to determine: • Which component of treatment actually caused observed changes • If observed changes would have occurred without intervention

  10. Baseline Data Collection • Baseline phase is a control period replacing a control group • Ethical considerations and baseline phase Not unethical to withdraw treatment for a short period when we are not sure of effectiveness of treatment

  11. Baseline Characteristics • Two characteristics of baseline data are important for interpretation of clinical outcomes: • Stability- Consistency of response over time • Trend- (slope) Shows the rate of change in the behavior

  12. Baseline Characteristics • The most desirable baseline pattern demonstrates: • A constant level of behavior • Minimal variability Indicating: Target behavior is not changing Therefore: Observable changes after intervention are due to intervention

  13. Baseline Characteristics • A variable baseline can present a problem for interpretation. • An Accelerating baseline-an increasing rate of response • A decelerating baseline-a decelerating rate of response • In both cases: a change in target behavior is occur13ring without intervention

  14. Length of Phases • Flexibility in considerations depending on: • Type of patient • Type of treatment • Expected rate of change in the target behavior It is essential that the length of time within each phase is sufficient to capture any changes

  15. Target Behavior • Can reflect: • Different response systems May focus on: Impairments functional limitations measures of disabilities Measurements may deal with overt motor behaviors- functional performance, ROM, gait characteristics

  16. Measuring Target Behavior • Frequency • Duration • Magnitude

  17. Frequency • Counting the # of occurrences of the behavior within: • A fixed time interval • Fixed number of trials • “Frequency count” is the simplest of all behavioral measures

  18. Frequency • Frequency count is appropriate to assess a discrete clinical behavior • Examples: • # of times a particular gait deviation occurs • # of times a client can repeat an exercise • # of times a patient loses her balance during a treatment session

  19. Frequency • Operational definitions for frequency counts must specify: • How the target behavior is distinguished from other responses • What constitutes an occurrence and nonoccurrence • (partial completion of exercise? fall over but catching oneself?)

  20. Frequency • “Frequency counts” are not useful when: • A behavior occurs too often to be counted reliably • A behavior lasts for a long time (occurs too seldom) The total time or total number of trials within which the count is made must remain constant across sessions

  21. Frequency • “Frequency counts” do not account for the quality of the behavior but only that it occurred • “Frequency counts”can be expressed as: • A percentage • Dividing # of occurrences by total # of opportunities (percentage correct)

  22. Frequency • Percentages are useful in that they are: • Easily understood • Efficient for summarizing large # of responses • Yet: If actual # of correct responses is an indicant of the target behavior, percentage can be misleading

  23. Frequency • “Frequency counts” can be translated into “rates” • The number of times a behavior occurs within a specific time period (seconds, minutes, hours) • Dividing the total # of occurrences by the total time • (Ambulation in steps per minute)

  24. Duration • Target behaviors can be measured according to how long they last • Duration can be measured either as: • The cumulative total duration of a behavior during a treatment session • The duration of each individual occurrences of the behavior

  25. Duration • How long a patient stays in a balanced standing posture within: • A treatment session • Or: • Time how long it takes for a patient to complete a functional task

  26. Duration • Can be reported in terms of percentages • “Percentage time in zone” • (Dividing total time in the desired zone by total time of training session) • This approach is useful when sessions are not of equal length

  27. Magnitude • Many clinical variables (target behaviors) are measured using instrumentation that provides quantitative data • (Electrical, functional performance)

  28. Interval Recording for Observational Measures • Target behavior are usually recorded using either: • Quantitative instrumentation • Appropriate for magnitude measure • Objective • Self-report • Monitor activities outside the clinical environment • Direct observation

  29. Interval Recording • Often recorded using frequency & duration methods to record the occurrence or nonoccurrence of the behavior • Certain behaviors are difficult to quantify • Break down the measurement period into preset time intervals • Determine if behavior occur or does not occur during each interval period (5 minutes)

  30. Interval Recording • Sometimes called “time sampling” • Total session time is divided into small equal intervals • Measurement may involve: • Recording the presence/absence of the target behavior within each interval, and then tallying how many intervals contained the behavior

  31. Interval recording • Recording the frequency or duration of the behavior within each each interval • It is important to select a time interval that will best reflect the expected frequency and duration of the behavior • Requires the use of a signaling device

  32. Reliability • Reliability is usually assessed concurrently with data collection, rather than in a separate pilot study • Reliability checks are performed by using two testers simultaneously observe the target behavior at several sessions across each phase

  33. Reliability • Interrater reliability is usually reported using a measure of percentageagreement between observers • Total Reliability • Total steps: A=25; B=28; • Total reliability: (25/28)x 100= 89% • Limitation: Reflects only the consistency of the total score for a session, but may observe different instances of the behavior

  34. Reliability • Point-by-Point/Interval-by-Interval/Trial-by Trial • Agreement is based on: Number of occasions on which the observers agree that a behavior occurred or not occurred is divided by total occasions that raters agree and disagree • Total 30 trials observers agreed on 29: • Trial-by-trial: (29/30) x 100= 97%

  35. Reliability • Interval-by-interval • Of 16 intervals (15 minutes), observers disagreed on 3 times (intervals 3,5,11) • (13/16)x 100= 81% • Chance agreement • Kappa – provides a statistical measure

  36. Experimental Control 1. A-B: Baseline-Intervention (before-after) 2. A-B-A: Baseline-Intervention-Baseline (Withdrawal design) If changes in behavior are not maintained during the second baseline phase- changes are due to intervention 3. A-B-A-B: • In 3, 4 designs, behavior must be reversible

  37. Experimental Control • Multiple Treatment Design 1. A-B-C-B: Two treatments have independent and differential effects 2. A-B-A-C: A second baseline phase between two treatments 3. A-B-C-A-C-B: Sequential relationship between B and C, and examine each treatment effect after baseline 4. A-B-C-BC: Combined phase

  38. Data Analysis • Analysis is based on evaluation of measurements within and across design phases to determine if: • Behaviors are changing • Observed changes during intervention are associated with the onset of treatment

  39. Data Analysis • 1. Visual analysis • No mathematical operations • Intuitively meaningful • Data within a phase are described according to: • Stability or variability • Trend- direction of change • Level- changes in magnitude (the value of the behavior) from last data point of one phase to another

  40. Data Analysis-Visual Analysis • Trend- direction of change within a phase • Accelerating or decelerating • Stable (constant) rate of change • Linear or curvilinear • A trend in baseline data: • No serious problem if against what is expected during intervention • A slope of a trend can only be determined for linear data

  41. Single-Subject Design • Now you know all about single-subject design

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