section 5-experimental design

Question 1

experimental control
functional relations analysis
control

Answer 1

• 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)

Question 2

4 important elements of B

Answer 2

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)

Question 3

what to do when seeing variability in data

Answer 3

• 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

Question 4

6 components of experiments in ABA

Answer 4

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

Question 5

experimental question

Answer 5

• 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

Question 6

1. at least 1 subject
   SINGLE-case designs
   within-subject designs
   intra-subject designs

Answer 6

• 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

Question 7

2. at least 1 B (DV)

Answer 7

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

Question 8

3. at least 1 setting

Answer 8

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

Question 9

4. at least 1 treatment (IV) / experimental varibale

Answer 9

• the particular aspect of the ENVIRONMENT that is MANIPULATED to find out whether it affects the subject’s B

Question 10

5. a measurement system & ONGOING analysis of data

Answer 10

• conduct observation & recording in a standardized manner (every aspect of the measurement: define B, schedule of observations)
• detect changes in LEVEL, TREND, VARIABILITY

Question 11

6. 1 experimental design

Answer 11

• 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

Question 12

component analysis

Answer 12

• 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

Question 13

steady state responding

stable state responding

Answer 13

• 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

Question 14

baseline logic

Answer 14

• 3 elements: prediction, verification, replication

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

Question 15

steady state strategy

Answer 15

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

Question 16

function of baseline data

Answer 16

• serves as a control condition

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

Question 17

benefits of baseline data

Answer 17

• 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

Question 18

4 patterns of baseline data

Answer 18

1. descending
2. ascending
3. variable
4. stable

Question 19

1. descending baseline

Answer 19

• 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

Question 20

2. ascending baseline

Answer 20

• 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

Question 21

3. variable baseline

Answer 21

• 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

Question 22

4. stable baseline

Answer 22

• 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

Question 23

3 parts of baseline logic

Answer 23

in successive order:

1. prediction
2. verification
3. replication

Question 24

baseline logic: prediction

Answer 24

• 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?*

Question 25

baseline logic: affirmation of the consequent

Answer 25

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

Question 26

baseline logic: verification

Answer 26

- (reverse design) terminate/withdraw the treatment variable to verify a previously predicted level of baseline responding

Question 27

baseline logic: replication

Answer 27

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

Question 28

5 main experimental design

Answer 28

MC RAW 1. multiple baseline 2. changing criterion 3. reversal 4. alternating treatments 5. withdrawal

Question 29

5 main experimental design 1. multiple baseline - most WIDELY used design

Answer 29

- 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

Question 30

a. multiple-baseline across Bx

Answer 30

- 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

Question 31

b. multiple-baseline across SETTINGS

Answer 31

- 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

Question 32

c. multiple-baseline across SUBJECTS | * most widely used multiple baseline design*

Answer 32

- 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

Question 33

2 weaker variations of multiple baseline design

Answer 33

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

Question 34

1. multiple PROBE design

Answer 34

- 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.

Question 35

2. DELAYED multiple baseline design

Answer 35

- 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

Question 36

guideline for multiple baseline design

Answer 36

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

Question 37

advantages of multiple baseline design

Answer 37

- successful intervention do NOT have to be removed (no reverse) - evaluates generalization - easy to implement

Question 38

disadvantages of multiple baseline design

Answer 38

- 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

Question 39

5 main experimental design: | 2. changing criterion design

Answer 39

- 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

Question 40

demonstrate functional relations in changing criterion design

Answer 40

- 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

Question 41

guideline for changing criterion design

Answer 41

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

Question 42

advantage of changing criterion design

Answer 42

- NOT require reversal of improved behavior | - enable experimental analysis within the context of a gradually improving B

Question 43

disadvantage of changing criterion design

Answer 43

- 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

Question 44

changing criterion design vs. shaping

Answer 44

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

Question 45

5 main experimental design: | 3. reversal design

Answer 45

- 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

Question 46

demo functional relations in reversval designs

Answer 46

- prediction-verificaiton-replication | - if repetition of baseline & treatment phases approximate the original phases --> IV is responsible for B change

Question 47

5 variations of reversal design

Answer 47

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

Question 48

5 variations of reversal design: | 1. repeated reversal

Answer 48

- simple extension of ABAB, e.g. ABABABAB - more reversal, stronger evidence of control - redundancy may be concerns

Question 49

5 variations of reversal design: | 2. BAB reversal

Answer 49

(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

Question 50

5 variations of reversal design: | 3. multiple treatment design

Answer 50

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

Question 51

5 variations of reversal design: | 4. non-contingent R (NCR) reversal design

Answer 51

- 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

Question 52

5 variations of reversal design: | 5. DRO/DRI/DRA reversal technique

Answer 52

- 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

Question 53

advantage of reversal design

Answer 53

- 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

Question 54

disadvantage of reversal design

Answer 54

- 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

Question 55

5 main experimental design: 4. alternating treatments design simultaneous treatments design concurrent schedules design multi-element (baseline) design multiple schedules design

Answer 55

- 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

Question 56

demo functional relations in alternating treatments design

Answer 56

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

Question 57

3 variations of alternating treatment design

Answer 57

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

Question 58

3 problems avoided by alternating treatments design

Answer 58

1. irreversibility 2. sequence effects 3. unstable data

Question 59

advantage of alternating treatments design

Answer 59

- 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

Question 60

disadvantage of alternating treatments design

Answer 60

- 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

Question 61

5 main experimental design: | 5. withdrawal design

Answer 61

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

Question 62

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

Answer 62

*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

Question 63

why ABA not use group approach to research

Answer 63

- group data not representative of individual performance - group data masks variability - absence of intrasubject replication

Question 64

2 types of validity in experimental design

Answer 64

1. internal validity | 2. external validity

Question 65

1. internal validity

Answer 65

- 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

Question 66

4 confounding threats to internal validity

Answer 66

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

Question 67

confounding vs. extraneous variables

Answer 67

- 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

Question 68

external validity generalizable to the external world

Answer 68

- 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

Question 69

treatment integrity procedure fidelity fidelity of implementation program integrity

Answer 69

- 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

Question 70

2 types of errors in evaluation ABA research

Answer 70

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