section 5-experimental design Flashcards
experimental control
functional relations analysis
control
- 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)
4 important elements of B
- individual
* group of ppl do NOT behave*
- a person’s interaction with the environment
- ABA experimental strategy is based on SINGLE-subject methods - continuous
- CHANGE over time
- requires continuous measurement over time - 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 - 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)
what to do when seeing variability in data
- 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
6 components of experiments in ABA
- at least 1 subject
- at least 1 B (DV)
- at least 1 setting
- at least 1 treatment (IV)
- a measurement system & ONGOING analysis of data
- 1 experimental design
experimental question
- 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
- at least 1 subject
SINGLE-case designs
within-subject designs
intra-subject designs
- 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
- at least 1 B (DV)
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
- at least 1 setting
control 2 sets of environmental variables to demonstrate experimental control:
- IV (present, withdraw, varied values)
- 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
- at least 1 treatment (IV) / experimental varibale
- the particular aspect of the ENVIRONMENT that is MANIPULATED to find out whether it affects the subject’s B
- a measurement system & ONGOING analysis of data
- conduct observation & recording in a standardized manner (every aspect of the measurement: define B, schedule of observations)
- detect changes in LEVEL, TREND, VARIABILITY
- 1 experimental design
- 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
- NONparametric analysis: IV either present or absent
- parametric analysis: manipulated IV value to discover the DIFFERENTIAL effects of a range of values
component analysis
- 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
steady state responding
stable state responding
- 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
baseline logic
- 3 elements: prediction, verification, replication
- each element depends on an overall experimental approach called steady state strategy
steady state strategy
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
function of baseline data
- serves as a control condition
- NOT imply the absence of INTERVENTION, can be the absence of a specific IV
benefits of baseline data
- 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
4 patterns of baseline data
- descending
- ascending
- variable
- stable
- descending baseline
- 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
- ascending baseline
- 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
- variable baseline
- 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
- stable baseline
- 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
3 parts of baseline logic
in successive order:
- prediction
- verification
- replication
baseline logic: prediction
- 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?*
baseline logic: affirmation of the consequent
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
baseline logic: verification
- (reverse design) terminate/withdraw the treatment variable to verify a previously predicted level of baseline responding
baseline logic: replication
- replication is the essence of BELIEVABILITY
- shows RELIABILITY of behavior change: can make it happen again
- reintroduce the IV to achieve replication
5 main experimental design
MC RAW
- multiple baseline
- changing criterion
- reversal
- alternating treatments
- withdrawal