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BCBA > section 5-experimental design > Flashcards

section 5-experimental design Flashcards

1
Q

experimental control
functional relations analysis
control

A
  • when a PREDICTABLE CHANGE (DV) can be RELIABLY produced by SYSTEMATIC MANIPULATION of some aspect of the individual’s ENVIRONMENT (IV)
  • analysis dimension of the 7 dimensions of ABA (BATCAGE)

Behavioral (observable & measurable)
Applied (socially significant Bx)
Technological (replicable)
Conceptually systematic (tie to basic principle)
Analytical (functional relationship/believability)
Generality (time/setting/behaviours)
Effective (practical)

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2
Q

4 important elements of B

A
  1. individual
    * group of ppl do NOT behave*
    - a person’s interaction with the environment
    - ABA experimental strategy is based on SINGLE-subject methods
  2. continuous
    - CHANGE over time
    - requires continuous measurement over time
  3. determined
    - the occurrence of any event is determined by the FUNCTIONAL RELATIONS it holds to other events
    - B is NATURAL phenomenon & subject to the same natural laws as other natural phenomena
  4. extrinsic to the organism
    - variability (change in B) is the result of the environment: IV, some uncontrolled aspect of the experiment, uncontrolled factors outside experiment (e.g. weather)
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3
Q

what to do when seeing variability in data

A
  • should attempt to manipulate factors suspected of causing the variability in the data to look for the causal factors
  • seek treatment variable robust enough to overcome variability
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4
Q

6 components of experiments in ABA

A
  1. at least 1 subject
  2. at least 1 B (DV)
  3. at least 1 setting
  4. at least 1 treatment (IV)
  5. a measurement system & ONGOING analysis of data
  6. 1 experimental design
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5
Q

experimental question

A
  • ALL well planned experiments begin with the experimental question
  • brief & specific statement of what researchers want to learn from conducting the experiment
  • in question / statement form
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6
Q
  1. at least 1 subject
    SINGLE-case designs
    within-subject designs
    intra-subject designs
A
  • ABA uses SINGLE-subject design, does NOT use group comparison*
  • the subject acts as one’s own CONTROL
  • does NOT mean there’s only 1 subject, usually involves more than 1 subject (commonly 4-8)
  • REPEATED measure of the subject’s B during each phase of the study –> provide the basis for comparing EXPERIMENTAL VARIABLES (IVs) –> present / withdraw the IV in subsequent conditions
  • the individual is exposed to EACH condition SEVERAL times over the study
  • each subject’s data are graphed SEPARATELY
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7
Q
  1. at least 1 B (DV)
A

some studies measure more than 1 DVs:

  • to provide data patterns that can serve as controls for EVALUATING & REPLICATING the effects of an IV
  • assess if any COLLATERAL EFFECTS: when the IV affects Bs other than the targeted B
  • to determine whether changes in the B of a person other than the subject occur during the experiment & if such changes can explain changes in the subject’s B
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8
Q
  1. at least 1 setting
A

control 2 sets of environmental variables to demonstrate experimental control:

  1. IV (present, withdraw, varied values)
  2. extraneous variables: prevent UNPLANNED environmental variation
  • when unplanned variations occur, you MUST try to wait them out/incorporate them into the design.
  • REPEATED measures tell whether unplanned environmental changes are of concerns
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9
Q
  1. at least 1 treatment (IV) / experimental varibale
A
  • the particular aspect of the ENVIRONMENT that is MANIPULATED to find out whether it affects the subject’s B
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10
Q
  1. a measurement system & ONGOING analysis of data
A
  • conduct observation & recording in a standardized manner (every aspect of the measurement: define B, schedule of observations)
  • detect changes in LEVEL, TREND, VARIABILITY
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11
Q
  1. 1 experimental design
A
  • particular arrangement of conditions in the study to have a meaningful comparison of effects of IV: present, absent, varied values
  • change ONLY 1 variable/1 treatment package/1 behavioral package at a time
    e. g. entire package: a token economy + praise + time-out
  • select & combine designs best fit the research
  1. NONparametric analysis: IV either present or absent
  2. parametric analysis: manipulated IV value to discover the DIFFERENTIAL effects of a range of values
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12
Q

component analysis

A
  • looks at the effect of each part of a treatment package/behavioral package
  • determine the effective components, keep the effective components & get rid of ineffective parts
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13
Q

steady state responding

stable state responding

A
  • a pattern of RESPONDING that exhibits very little variation in its measured dimensional quantities over a period of time
  • provides the basis for BASELINE LOGIC
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14
Q

baseline logic

A
  • 3 elements: prediction, verification, replication

- each element depends on an overall experimental approach called steady state strategy

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15
Q

steady state strategy

A

REPEATED expose a given subject to a given CONDITION
–> try to eliminate the EXTRANEOUS influence on B & obtain a STABLE pattern of responding before introducing the NEXT condition

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16
Q

function of baseline data

A
  • serves as a control condition

- NOT imply the absence of INTERVENTION, can be the absence of a specific IV

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17
Q

benefits of baseline data

A
  • use the subject’s performance in the absence of the IV as an objective basis for detecting change
  • obtain descriptions of ABC correlations for the planning of an effective treatment
  • guide to set the INITIAL CRITERIA for R
  • to see if the B targeted for change really warrants intervention
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18
Q

4 patterns of baseline data

A
  1. descending
  2. ascending
  3. variable
  4. stable
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19
Q
  1. descending baseline
A
  • shows the B is already changing
  • generally, one should NOT implement the IV when the baseline is descending
  • if the descending baseline is due to a behavior you want to decrease, you should wait coz the B is already improving
  • implement IV if you try to increase sth & the descending trend shows it’s worsening
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20
Q
  1. ascending baseline
A
  • shows the B is already changing
  • generally, one should NOT implement the IV when the baseline is ascending
  • if the ascending baseline is due to a behavior you want to increase, you should wait coz the B is already improving
  • implement IV if you try to decrease sth & the ascending trend shows it’s worsening
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21
Q
  1. variable baseline
A
  • NO clear trend
  • wait it out & do NOT introduce IV
  • assumed to be due to environmental variables that are UNCONTROLLED
  • if introduce IV now, will NOT be able to tell if it changed the B or not
  • should try to control UNCONTROLLED sources of variability
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22
Q
  1. stable baseline
A
  • NO evidence of ascending / descending trend
  • all DV values fall in a SMALL range
  • BEST way to look at the effects of IV on DV
  • can introduce IV NOW
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23
Q

3 parts of baseline logic

A

in successive order:

  1. prediction
  2. verification
  3. replication
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24
Q

baseline logic: prediction

A
  • anticipate outcome of a presently unknown measurement
  • data should be collected until STABILITY is CLEAR
  • the more data points, the better predictive power
  • are data stable enough to serve as the basis for experimental comparison?*
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25
Q

baseline logic: affirmation of the consequent

A

inductive logic:

  • if IV not applied, the B won’t change as indicated by baseline
  • experimenters predicts IV will change B
  • if IV is controlling DV (A), then data path with the presence of IV will show DV changes (B)
  • when IV presents, the data should DV changes: B is true
  • thus, the IV is controlling the DV: A is true
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26
Q

baseline logic: verification

A
  • (reverse design) terminate/withdraw the treatment variable to verify a previously predicted level of baseline responding
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27
Q

baseline logic: replication

A
  • replication is the essence of BELIEVABILITY
  • shows RELIABILITY of behavior change: can make it happen again
  • reintroduce the IV to achieve replication
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28
Q

5 main experimental design

A

MC RAW

  1. multiple baseline
  2. changing criterion
  3. reversal
  4. alternating treatments
  5. withdrawal
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29
Q

5 main experimental design
1. multiple baseline

  • most WIDELY used design
A
  • highly flexible
  • staggered implement the intervention in a step-wise fashion across B, SETTINGS, SUBJECTS
  • NO withdrawal/reverse session
  • *ethical**: when reverse conditions are unethical/impractical, when the B is irreversible, use multiple baseline design
  • demo FUNCTIONAL RELATIONS: requires a change in B with the onset of the intervention:
    apply IV to B1 when you confidently PREDICT that the B would remain the SAME in constant condition (stable responding in the baseline)
    –> if B2 & B3 remain unchanged after applying IV to B1, this VERIFY the predication
    –> if the IV changes B2 like it did to B1, the effect of the IV has been REPLICATED
    –> the more replications, the more convincing the demonstration.
  • most commonly 3-5 tiers
30
Q

a. multiple-baseline across Bx

A
  • 2 or more Bx of the SAME subject
  • each subject serves as one’s own CONTROL
  • after steady STABLE BASELINE responding, the IV is applied to the 1st B while other Bx are kept in baseline
  • when steady STABLE responding is reached for the 1st B, then the IV is applied to the next B
31
Q

b. multiple-baseline across SETTINGS

A
  • a SINGLE B is targeted in 2 or more different settings / conditions
  • after steady STABLE BASELINE responding, the IV is applied to the 1st setting while other settings are kept in baseline
  • when steady STABLE responding is reached for the 1st setting, then the IV is applied to the next setting
32
Q

c. multiple-baseline across SUBJECTS

* most widely used multiple baseline design*

A
  • a SINGLE B is targeted for 2 or more subjects in the SAME setting
  • after steady STABLE BASELINE responding, the IV is applied to the 1st subject while other subjects are kept in baseline
  • when steady STABLE responding is reached for the 1st subject, then the IV is applied to the next subjects
33
Q

2 weaker variations of multiple baseline design

A
  1. multiple PROBE design
  2. DELAYED multiple baseline design
  • both variations weaker than traditional multiple baselines
  • used when EXTENDED BASELINE measurement is UNnecessary, impractical, too costly, unavailable
34
Q
  1. multiple PROBE design
A
  • analyze relation between the IV & acquisition of skill SEQUENCES
  • probes provide the basis for determining if B change has occurred PRIOR to intervention
  • NO continuously measured baseline
  • B1 (probe 3 times): probe 3 times before implementing IV to B1–>no B change occurred
  • B2 (probe 2 times): probe 1 time at the beginning of the experiment, probe 1 more time before implementing IV to B2
  • B3 (probe 3 times): probe 1 time at the beginning of the experiment, probe 1 more time before implementing IV to B2, probe 1 more time before implementing IV to B3.
35
Q
  1. DELAYED multiple baseline design
A
  • initial baseline & intervention begin & subsequent baseline are added in a delayed/staggered fashion
  • effective when:
    a. reversal design is impossible
    b. limited resources preclude a full-scale design
    c. when a new B/subject/setting becomes available
  • limitations: shorter baselines do NOT show interdependent of DVs
36
Q

guideline for multiple baseline design

A
  1. select independent & functionally similar baselines
    • Bx are FUNCTIONALLY independent of one another
    • Bx share enough SIMILARITY –> they will change with the application of the SAME IV
    • Bx should be DIFFERENT response classes (i.e. same function within 1 response class) –> INDEPENDENT
  2. select concurrent & plausibly related multiple baselines
    • Bx must be measured concurrently
    • all relevant variables that influence 1 B must have the opportunity to influence other Bx
  3. do NOT apply the IV to the next B too soon
  4. vary significantly the LENGTH of multiple baselines
    • the more baselines differ in length, the stronger the design
  5. intervene on the MOST stable baseline first
37
Q

advantages of multiple baseline design

A
  • successful intervention do NOT have to be removed (no reverse)
  • evaluates generalization
  • easy to implement
38
Q

disadvantages of multiple baseline design

A
  • functional relationship is NOT DIRECTLY shown in this design (coz no reverse)
  • effectiveness of the IV is demonstrated but not information regarding the FUNCTION of the target B
  • IV may be delayed for certain B, settings, subjects
  • takes resources to implement properly
39
Q

5 main experimental design:

2. changing criterion design

A
  • baseline phase is followed by a series of treatment phases consisting of SUCCESSIVE & GRADUALLY changing criteria for R / P
  • only 1 B in the design
  • B has to already in the subject’s REPERTOIRE
  • evaluates treatment is applied in a GRADUATED/STEP-WISE fashion
  • technically, it’s a variation of the multiple baseline design

e.g. use it to assess how a person’s B changes when the researcher provides the person with R contingent upon 10 responses per minute, then 20 responses per minute, then 30 responses per minute, and so on

40
Q

demonstrate functional relations in changing criterion design

A
  • the criterion lines should have a LARGE SEPARATION to show a functional relationship
  • experimental control: the EXTENT that the level of responding changes to conform 遵从 to each new criterion
  • If data points do NOT fall around the criterion lines –> it shows there is very LITTLE experimental control
  • the greater the VERTICAL distance between the criterion lines, the MORE experimental control
41
Q

guideline for changing criterion design

A
  1. length of phases
    • each phase must be long enough to achieve STABLE responding
    • target Bx that are SLOWER to change require longer phases
    • VALIDITY of the design is INCREASED when you VARY the LENGTH OF each phase
  2. magnitude of criterion changes
    • the size of the changes between each criterion should VARY to prove strong functional relations
    • changes in size must be LARGE enough to be detectable, but not so large as to be unachievable
      * changes in size can be smaller if dealing with STABLE data
  3. number of criterion changes
    * the MORE criterion changes, the BETTER proof of experimental control
42
Q

advantage of changing criterion design

A
  • NOT require reversal of improved behavior

- enable experimental analysis within the context of a gradually improving B

43
Q

disadvantage of changing criterion design

A
  • target B must already be in the person’s repertoire
  • NOT appropriate for analyzing the effects of a shaping program (shaping is used to develop novel B)
  • NOT a comparison design
44
Q

changing criterion design vs. shaping

A

shaping:
- a B changing strategy but NOT experimental design
- used to teach NOVEL Bx: reinforcing responses that meet a gradually changing criterion (successive approximations) towards the terminal B
- changing response criterion: TOPOGRAPHICAL in nature, require different FORMS of B at each new level

changing criterion design:

  • is an experimental design that results in B change
  • can NOT use with NOVEl B
  • best for evaluating the effects of instructional techniques on step-wise changes in RATE, ACCURACY, DURATION, LATENCY of a single target B
45
Q

5 main experimental design:

3. reversal design

A
  • any experimental design that the researcher REVERSES responding to a level obtained in a PREVIOUS condition
  • IV is withdrawn (e.g. ABAB) or reversed in its focus (e.g. DRI incompatible B/DRA alternative B)
  • alternating between baseline & a particular intervention
  • each reversal strengthens experimental control & functional relation: switch from 1 condition to the other with a corresponding change in trend & level
  • for a reversal, B must approximate the initial baseline level
  • at least 3 consecutive phases: ABA
  • ABAB preferred over ABA as stronger design
  • most powerful WITHIN-SUBJECT design for demon functions

ethics
if client is displaying severe & dangerous Bx (e.g. SID, elopement)
–> NOT spend time just taking baseline from the start
–> ethical responsibility to get in & immediately provide treatment for health & safety of the client
–> can use BAB reversal

46
Q

demo functional relations in reversval designs

A
  • prediction-verificaiton-replication

- if repetition of baseline & treatment phases approximate the original phases –> IV is responsible for B change

47
Q

5 variations of reversal design

A
  1. repeated reversal
  2. BAB reversal
  3. multiple treatment design
  4. NCR reversal technique
  5. DRO/DRA/DRI reversal technique
48
Q

5 variations of reversal design:

1. repeated reversal

A
  • simple extension of ABAB, e.g. ABABABAB
  • more reversal, stronger evidence of control
  • redundancy may be concerns
49
Q

5 variations of reversal design:

2. BAB reversal

A

(B) IV implemented –> (A) IV removed –> (B) IV reintroduced

  • weaker than ABA design coz does not enable assessment of the effects of IV during baseline
  • best design when client displays severe & dangerous B
  • appropriate when IV is already in place & you have limited time
  • disadvantage: SEQUENCE EFFECTS coz the level of B in condition A may be influenced by the IV before it
  • sequence effects/alternation effects/carryover effects*
  • effects on a subject’s B in a given condition that are the result of the subject’s experience with a PRIOR condition
50
Q

5 variations of reversal design:

3. multiple treatment design

A
  • compares 2 or more IVs to baseline and/or to one another
    e. g. ABACABAC, ABCDACAD
  • disadvantage: SEQUENCE EFFECTS
51
Q

5 variations of reversal design:

4. non-contingent R (NCR) reversal design

A
  • shows the effect of R by using NCR as a CONTROL condition instead of baseline with no R
  • allows examining contingent R
  • the reinforcer is presented in fixed/variable time schedule INDEPENDENT of the subject’s B

e.g. baseline–>contingent R–>NCR–>contingent R–>NCR

52
Q

5 variations of reversal design:

5. DRO/DRI/DRA reversal technique

A
  • shows the effects of R by using DRO, DRI, DRA as CONTROL condition instead of baseline with no R
  • allows examining contingent R
  • DRO: R any B other than the target B
  • DRI: R B that is physically INCOMPATIBLE with the target B
  • DRA: R an alternative B other than the target B
53
Q

advantage of reversal design

A
  • clear demo of the existence / absence of a functional relations between IV & DV
  • enable us to count the amount of B change
  • return to baseline tells: we need to program for maintenance
54
Q

disadvantage of reversal design

A
  • irreversibility: the level of B observed in an earlier phase can NOT be reproduced even though experimental conditions are the same as the earlier phase

ethics
remove an effective IV can cause ethical, social, educational issues

55
Q

5 main experimental design:
4. alternating treatments design

simultaneous treatments design
concurrent schedules design
multi-element (baseline) design
multiple schedules design

A
  • 2 or more conditions are presented in RAPIDLY alternating succession INDEPENDENT of the level of responding & the differential effects on the target B
  • compare 2 or more IVs to see which IV is best
  • based on stimulus discrimination: each IV has an obvious SD signaling which IV is in effect at a given time
  • data for each IV are plotted SEPARATELY on the SAME graph
  • IVs maybe alternated across daily sessions/given in sessions occurring the same day/implement during each portion of the same session
56
Q

demo functional relations in alternating treatments design

A

on graph:

  • visual inspecting the differences between / among the data paths produced by each treatment
  • functional relations demo when: 1 data path is CONSISTENTLY higher than the other & NO OVERLAPPING
  • the degree of differential effects produced by 2 treatment is determined by the VERTICAL distance between the data paths
  • prediction/replication/verification is NOT identified in separate phases.
  • each successive data point plays all 3 roles
57
Q

3 variations of alternating treatment design

A
  1. single phase without baseline: no initial baseline
  2. with baseline: with initial baseline
  3. with baseline & final best treatment phase: most WIDELY used:
    initial baseline –> alternating treatments (e.g. T1, T2, T1>T2) –> T1
58
Q

3 problems avoided by alternating treatments design

A
  1. irreversibility
  2. sequence effects
  3. unstable data
59
Q

advantage of alternating treatments design

A
  • NOT require treatment withdrawal
  • speedy comparison
  • min. irreversibility problem & sequence effects
  • can be used with unstable data
  • can be used to assess generalization of effects
  • intervention can begin IMMEDIATELY without baseline data
60
Q

disadvantage of alternating treatments design

A
  • multiple treatment interference as multiple treatments are going on at the same time
  • unnatural nature of rapidly alternating treatments
  • limited capacity of the design: max. 4 conditions
61
Q

5 main experimental design:

5. withdrawal design

A
  • aka: ABAB design
  • describe experiment that an effective treatment is sequentially / partially withdraw to promote the MAINTAINANENCE of B changes
62
Q

ethical considerations in single-case experimental designs to demo treatment effectiveness

A

1st goal is to CLEARLY show IV changes the target B & nothing else

3 concerns:

  1. baseline trends
    - increase/decrease trends in baseline do NOT allow for clearly demo IV cuauses the change in B
    - continue observe for longer time
    - try to reverse the trend, e.g. DRO
    - select designs that do NOT require stable baseline
    - use statistical technique to take initial trend into account
  2. excessive variability in data
    - variability in data can OBSCURE intervention effects
    - block consecutive data points, plot blocked AVERAGE rather than day-to-day performance
    - search for causes of the variability / the situation, e.g. environmental stimuli
  3. duration of phases
    - duration of each phase can involve problems related to trend & variability
    - no rigid rules abt how many data points need for each phase
    - objective criteria: use this to decide when to shift phases, help to reduce subjectivity
  • single subject designs best for treatment package evaluation
  • the generality of the research results: use replication of IV across subjects to assess the generality
63
Q

why ABA not use group approach to research

A
  • group data not representative of individual performance
  • group data masks variability
  • absence of intrasubject replication
64
Q

2 types of validity in experimental design

A
  1. internal validity

2. external validity

65
Q
  1. internal validity
A
  • the extent to which an experiment shows convincingly that changes in B are a function of the IV & not the result of uncontrolled/unknown variable
  • an internally validity study involves 1 IV at a time –> avoid confounding
  • high internal validity = designs show strong experimental control
66
Q

4 confounding threats to internal validity

A
  1. measurement confounds
    - # & the intricacy/complexity of targeted Bx, e.g. target numerous complicated Bx
    - observer drift: when observers unknowingly alter the way they apply a measurement system
    - reactivity: B of clients changing when observed
    • maintain baseline long enough to reduce reactivity
      - observer bias/expectations:
    • keep observer naive to expected outcomes of a study
  2. IV confounds
    - IVs are complicated & given tgt in a treatment package
    - reduce: placebo control/double blind control
  3. subject confounds
    - maturation 发酵，成熟: changes in subject over course of study
    - REPEATED measurement detects uncontrolled variables
  4. setting confounds
    - studies in natural settings are more prone to confounding variables
    - should hold all possible aspects of the study CONSTANT until repeated measurements again reveal stable responding
    - BOOTLEG R may occur in the natural environment: secretive R that is not part of your B plan
67
Q

confounding vs. extraneous variables

A
  • aka for uncontrolled influence on a research study
  • should be reduced / eliminated as much as possible to demo experimental control

extraneous variable: any aspects of the ENVIRONMENT that must be held CONSTANT to prevent unplanned environmental variation
e.g. light, space, the temperature of the room

confounding variable: any uncontrolled factor known / suspected to EXERT influence on DV

68
Q

external validity

generalizable to the external world

A
  • the degree that a study’s results are generalizable to other subjects, settings, Bx
  • the degree that a functional relation discovered in a study will hold under different conditions
  • external validity is range from a little to a lot
  • REPLICATION establishes external validity
    1. direct replication: exactly duplicated
    intrasubject or intersubject
    2. systematic replication: purposefully varies 1 or more aspects of an earlier experiment
    • demo RELIABILITY & external validity: showing same effect occur under different conditions
    • generally used in ABA
69
Q

treatment integrity

procedure fidelity
fidelity of implementation
program integrity

A
  • the extent that the IV is implemented/carried out as planned
  • low treatment integrity: very difficult to interpret experiment results
  • TREATMENT DRIFT: when the application of the IV in later phrases differs from the original application
  • ensure high treatment integrity*
  • precise operational definition of treatment procedures
  • simplify, standardize, automate: simple treatment is more likely to be consistently delivered; simple & easy to implement techniques are more likely to be used & socially validated
  • training & practice: detailed script, verbal instructions etc.
  • assess treatment integrity*
  • observation & calibration: ongoing retraining & practice to ensure high treatment integrity
  • reduce, eliminate, identify the influence of potential confounding variables
70
Q

2 types of errors in evaluation ABA research

A
  1. type I error/false positive
    • statistical analysis tends to lead to more type I error
  2. type II error/false negative
    • visual analysis in ABA tend to lead to more type II error

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