Discussion Post. 150-225 words.
Will be crossed checked through turnitin.com and coursehero.
1. Paraphrased and cited in APA style the explanation of the environmental conditions in which steady responding occurs .
2. Identify 2 reasons why an investigator should be concerned about trends in the data that have no obvious explanation and what a practitioner can do about it.
3. Provide a solution for one of your hypothetical concerns.
CHAPTER NIN
E
Stead
y
States and Transitions
THE STEADY-STATE STRATEGY
Collecting Repeated Measures
Comparing States of Responding
The Risk of Extraneous Variables
Summar
y
STEADY STATES
Definition
Uses
Evaluates Measurement Decisions
Reveals the Influence of Conditions
Evaluates Experimental Contro
l
Facilitates Experimental Comparisons
Identification
Trends
Range
Cycles
Criteria
Uses
Statistical
Graphical
Nondata
Establishing Stable Responding
TRANSITIONS
Transition States
Transitory States
Identification
Making Phase Change Decisions
191
I
192
9 . S1′ EADY STATES AND TRANsrr
IONs
1,.
Manipulation of new variables will often produce changes, but tn
order to describe tbe cbanges, we must be able to specify the baseline
0111 wbicb tbey occurred.
-Murray Sidman
1HE STEADY-STATE STRATEGY
Collecting Repeated Measures
Let us suppose that we have defined a response class, selected a dimensional
quantity, set up observation procedures, and are ready to sta~ collecting data
under a baseline ( control) condition. The first graphed data point summarizing
responding during a session will tell us something we never knew before, but it
will only make it obvious that one data point does not tell us very much about
what responding looks like under this condition. In particular, we would not
know whether this value is typical of what we should expect under this base
line condition.
The only way to answer this question is to observe for another session. What
we are likely to find is that our second data point is not the same as the first.
Our question then becomes: “Which of these two values is more representative
of the impact of this phase?” Again, there is no way to settle this issue except to
observe for another session.
We should not be surprised if the third value is at least somewhat different
from the other two. However, if the three values are not wildly different,
they may begin to tell us something about responding in this phase. Still, it
would be easy to admit that we do not yet have a very complete picture of
what responding is like in this phase. After all, our participant has had only
limited exposure to this condition, and we know it can take a bit of time for
responding to adapt to a new set of influences. In other words, there is good
reason to anticipate that the initial impact of our baseline condition may not
be a ~ery good prediction of how responding might change with increasing
experience.
As we keep collecting data from one session to the next, our graph will
gradually draw an increasingly comprehensive picture of responding. With
some luck, we may find that responding under this initial condition is relatively
:able. This means t~t ~~sponding is neither generally increasing nor decrea~
~ and th~t the variability from one value to another is not excessive and 15 i
fat~l~ consistent. We may even begin to feel some confidence in answering the
ongmal f ·Wh · hi 5 .. ques ion. at kind of responding represents the typical impact oft
cond1tton?
193
THE STEADY-STATE STRATEGY
BOX 9.1
Measuring One Participant M .
Many Participants Once any Tunes versus
There are two different approach ..
· al es to obta1111ng . experunent conditions on respond· U a picture of the effects of
di . th h mg. nder both cont 1 d . tions, e researc er can measure th b h . ro an treatment con-
. · e e avior of one p rt· · or many participants once. Although . a icipant many times
there is a big difference between t:ou c: wmd up with lots of data either way;
things about behavior. ese ternatives for our ability to discove;
To understand this argument rem b h .
‘ em er t e disc · · fact that behavior is a biological ph uss10n m chapter 2 about the
enomenon This h
dearly observed only at the level of th . d’ . · means t at behavior can be
influence of any variables on behav· e m iVIb dual organism. In other words, the
. tor can e clearly n1 .
the behav10r of each participant. Althou h . seen ~ Y as they impact
variables would affect different partici g. we nught wish that treatment
assume that thi ill b h pants m exactly the same way; we cannot
. s w e t e case. In fact, this is part of what we are trying to learn
and usmthis~ group~d data from many different individuals makes it difficult t;
answer quest10n.
As this chapte~ ~how~, observing the behavior of a single participant repeat
edly under~ condition gives the researcher the opportunity to obtain a complete
and clear picture ~f the effects of that condition on responding. It should be easy
to se~ that ob~ervmg the behavior of a participant only once cannot provide the
same inform:itt~~. It may not be so obvious that measuring the behavior of a large
number of mdividuals once for each does not improve the completeness or
clarity of the picture of the effects of the condition on behavior. Although this
tactic would provide many observations, each would show only the smallest
sample of how different participants, each with his or her unique characteristics
and histories, might react to the condition. We would know no more about the
effects of the condition on the behavior of each participant than we would if we
measured a single individual once. In other words, the point is not merely to get a
lot of data but to get enough of the right kind of data. What we need are data that
reveal exactly how each participant’s behavior is influenced by a condition.
Comparing States of Responding
With this answer, we may decide that we are ready to see how responding
changes when our participant encounters an intervention condition. In order
to make this comparison, we must first determine what kind of responding
is typical of the effects of this new condition. As we accumulate data points
across repeated sessions, the graph will gradually reveal a new picture of
responding. we might find that responding is initially like that observed in
~he first phase but gradually transitions to a differe?t level. On the other hand,
it might be that responding immediately changes m some way. Whatever the
9. STEADY STATES AND TRANsn10Ns
194
uld probably find that the more sessions we observe th . ‘tial changes, we wo . di . , e 1111 ‘ d t d the effects of the intervention con t10n.
better we un ers an fir h b 1·
. . art’ cipant repeated exposure st to t e ase me condir
By g1vmg our p 1 . hi ton
d t1 to the intervention condition, we are trying to get a grap cal Picture
an
1
en di der each condition that is complete and representative Th
of respon ng un b nfid . at
. . t t make sure we obtain enough data to e co ent that we hav
1s we wan o h d’ · aft · e
Ie~ned what responding looks like unde~ ea~ con 1t10n er its effects on
the target behavior are fully developed. This will allow us to ~ompare respond
. under the two conditions and be sure we are comparing data that fully
mg Thi · · rt t b represent the impact of each condition. s ts tmpo_ an ecause we Want to
be able to conclude that any differences in responding we see are due to the
differences between the two conditions themselves.
The Risk of Extraneous Variables
What complicates this conclusion is the risk that responding under either
condition might be influenced not just by the condition itself, but by extrane
ous factors. Making repeated observations of a participant’s responding under
each condition provides one way of assessing this risk. This approach depends
on trying to make sure there is relatively little variation from session to session
in the key features that define each condition. This certainly does not mean
that a participant’s experiences are identical across sessions within each of
these two phases. However, it does mean that the key factors that make up each
condition are relatively consistent from session to session.
Given this consistency, if the data show that responding is unstable under
either condition, we should assume that there are factors responsible for these
variations in responding. We might reason that if these variations are not due
to changes in the condition, which is being held constant, they must be due to
extraneous factors. More optimistically, if the data are relatively stable during a
condition we might assume that either extraneous factors are not having
noticeable effects or that any extraneous effects are at least consistent.
In other words, when the data within a phase are relatively stable it provides
limited assurance that extraneous influences are relatively minor. As we shall
see, this conclusion is not necessarily true. However, it is at least reassuring that
the data are not noticeably or systematically variable. This would leave us no
choice but to worry that extraneous factors may be causing this variability.
Uns~ble patterns of responding from session to session in a phase would
req~~ us to admit that the data may represent not just the effects of the
condition, but the effects of the extraneous factors as well. If this were the case,
we woul~ ~heref?re not be in a good position to compare responding ~der
that condition with responding under another condition. Such a comparison
wo~d not allo_w us to conclude that differences in responding were due onlY
to differences m the conditions. .
In other words, a graphical picture of stable responding across sessions
under a c d’t’ be on 1 ion provides some encouragement that the data represent t
fJ-lE STEADY-STATE STRATEGY
-‘Jects of that condition and th 195
o. l at the c .
rniniIDal or at east constant. Stable 0 ntribution of e
j(ind of marker for two important ;esponding-a stead xtrane~us factors is
ssed, stable responding sugg c 1aracteristics of tlley 1state-
1s therefore a
Cu ests th t c ata Fir t
unJess they are consistent through a extraneous influ · s , as just dis-
out the c di . ences are mi · 1 suggests that any transition from the . . . on tton. Second, stabl ~a ,
enduring effects is complete. 1111ttal effects of the cond’t’ e res~ondmg
i ton to its more
summary
The steady-state strategy is an
. th . approach to kin
1sons at mvolves ·measuring resp d’ ma g experimental compar-
under both control and experimental on md·g· for each participant repeatedly
con itions in .
to assess an d manage extraneous infl succession. The objective is
uences and the b b .
of respon din g that represents the full ft; re Yo tam a stable pattern
evolved in the work of B F. Skinn de hi~cts of each condition. This strategy
· · er an s st d ·
first described in detail by Sidman
O
9
60
) h u ents (Skinner, 1956), and was
1
way of managing extraneous influ · t as been a powerful and effective
ences and obtainin . 1 effects of each condition. This outcome all g a _c ear picture of the
under control and intervention conditions ;:: ~ompansons of r~sponding
~~~ ::tr treatment variables. This focus :i = i:ili~~!~~c:::7i:
. . e Y to hold up when tested by other researchers or used by
practitioners.
The steady-state strategy is equally useful in basic and applied research
projects. Although it can be more challenging to obtain stable responding
in nonlaboratory settings, the costs of failing to do so are unavoidable. If
researchers collect only a few observations under a condition, the data cannot
help to identify extraneous influences and will not provide a full picture of the
effects of that condition. This limitation increases the risk that comparisons of
responding under control and treatment conditions will be misleading. As a
result, when others use this information, there is a greater chance they will not
get the same results.
Practitioners often have the opportunity to measure a client’s behavior
repeatedly over a number of days or even weeks. A baseline or pretreatment
phase is typically followed by an initial intervention designed to change
responding in some practical way. The initial treatment procedure is often
followed by adjustments required to make the procedure more effective.
9, STEADY STATES AND l’RANS)1’JONs
19 6
. also be needed to accommodate changes in the behavior or
AdJustments ~ay s the intervention proceeds. Repeated measUr
uncling crrcumstances a . h e-
surro h h allow practitioners to momtor c anges in th ments throughout eac P ase ~ . e
h · t11e project continues.
target be avior as . interest of practitioners is delivering effective se
Of course, tl1~ pnmaryrun· ental comparisons for the purpose of publishinr
vices not arrangmg expe d · f£ t t d · · g
rese~ch findings. This obligation usually iscodurah ges e or hs o istmguish
f·~ t f …. eatment procedures an t e many ot er events going W.’ between tl 1e e .iec so h . .
. Ii d settings Decisions about when to make c anges m conditions are
on m adripp e b · clini’cal considerations than by steady-state criteria. As a often ven more y . .
Service delivery priorities, practitioners are not usually in ul f th res t o ese h h . Ii ‘ b
a strong pos1 ·u·on to be confident about exactly w Y t err c ent s ehavior
changes. This is simply one of the distinctions between research and practice.
STEADY STATES
Definition
A steady (stable) state of responding may be defined as a pattern of respond
ing that shows relatively little variation in its measured dimensional quantities
over some period of time. Let us examine some implications of this definition.
I Stea~y s_~e. A pattem~t r~~~wia]l~t\f s~~r,~1~iiv~; li~-;:·1
! vanat,or., m ,ts m~asured d1me.11~19p~Lq~~!J!t!l~~i-£?~~r;.:~or.n..~ per~od ‘ .. 1
0 Lf time· .. _ ·~-~:. ,_~_;-· _ ~~~-:_> ~· ;~L;;<::~S2}l1~bt1~i~.~~s~t~\~t~\;:~~;¢IU1-~~~~~-J First, it is important to understand that the meaning of the term steady is
relative. Exactly how much variability or what patterns of variability are
required to describe responding as stable or unstable will vary from one
research project to another. Such factors as the characteristics of the response
class, the features of the general environment, the details of experimental pro
cedures, and the focus of the experimental question may influence the
researcher’s judgment. For example, an investigator conducting a laboratory
study of the effects of toxic chemicals on the behavior of rats may expect a
very low level of variability, given similar studies already published. On the
ot~er hand, a researcher conducting a project in a group home for individuals
with mental retardation may need to accommodate the effects of day-to-day
variations in extraneous factors that cannot be easily managed. Such dif
ferences from one study to another mean that it is usually not feasible to define
steady-state responding with a rigid formula .
. Second, although the dimensional quantity being measured might be stable,
this does not mean that other quantities not being measured are also stable.
F~r example, although the number of responses from session to sessi~n
might show good stability, their duration could be systematically changing i1l
\ ,
I\ cADY sTA’fES
5’fw
n1e way. In fact, when two or m 197
soJ…- ore quanrt·
conunon for them to vary in dif~ 1 1es are being tU1 h . . 1.erent way measured 1 ·t. O
lrnOW about t e stability of quant·t· S. f course th . , 1S not
iv• • 1 1es that , ere 1s no
. whY evidence of stability in a d” are not being way to
5 to the generalimensional quantity shoufdeasured. This ~e researcher make
. . . statement h not prompt
:cnstead, 1t 1s more appropnate to say th at a particula t at respondin g ts . stable
stable. r feature of respond· ..
‘fhird, just because some aspect of mg ts respondin ·
to conclude that the environment i’s al g is stable, it may not b . so stable A . e correct 1
of responding can result from a mix of h . · re at1vely consistent patt c angmg v . bl ern
stable respon din g. Some environment 1 f: actors m aria es whose net effect · a ts
ways but not inf] uence the target beh . ay even change in obvious . av1or. For exa 1 . wo seem to e an rmportant chan . mp e, a substitute teacher b uld
ever, this change may not be evident ~e ~ a classroom environment. How
experimental environment from ob Ill .t e data. All we can say about the
extraneous environmental changes a:rvi_ng stable responding is that any
have effects that are balanced by oth ect~g responding are either weak or
er environmental factors.
Uses
Evalu~tes ~easurement Decisions. The steady-state strategy is valuable
because it . gutdes the researcher’s decisions as the study progresses. This
benefit begms by helping the researcher to evaluate prior decisions about how
the target behavior is defined and measured. Collecting data under such rules
for a number of sessions provides a picture of responding that may prompt
second guesses about how the response class is defined, which dimensional
quantities are measured, and how and when observation is conducted.
For example, the pattern of variability across sessions might suggest
reconsidering the response class definition. If the data tend to fall into distinct
patterns from one session to another (such as higher versus lower values),
it could mean that the definition has combined different functional classes.
For instance a definition of “aggressive” behavior may include both hitting
and cursing. In some sessions, the target responses may be largely in the form
of hitting, and on other days measured responses ~ay be mo_st~y cursing. If
cursing rail t nds to occur at higher frequenaes than hitting, the data
gene ye h h Th … · ns -ri’th higher values t an ot ers. at 1s, sessions m
s ow some sess10 – . Could h -.ainly in the form of cursing would have higher values whi h d’
c respon mg was hi u… h was 1 hi ttmg. · Thi pattern f . . responding most y s o
th an . sessions . U1 w c t d ata are suggestmg v . . t the researcher to wonder if h e ·
a ari.ability llll;ght proi:e target behavior is defined. Perhaps it would be more
problem with hoW e hitting and cursing separately.
useful to define and measurasures under each condition can also encourage
Collecting repeated me urement decisions. For example, if the data showed
curiosity about other meas ne session to another, it might be tempting to
very little change frc:,1 °was stable. We have already pointed out, however,
conclude that respon wg
9, STEADY STAT.E~ ANU TllANSITJONs
198
. de assurance that the dimension quantity be·
t ble data o n1 Y pro Vl fi . ltlg
that s a . bl S h data may not reveal the reason or this stabiU
sured 1s sta e. uc . · · h ty
mea . f hat is happening dunng sessions m1g t show th ·
T-~ al observation o w . . . at
iu.1orm b h . var1· es a good bit from session to session m other Way
the target e av1or . Id s.
ether with the overly stable data, these observatt~ns cou suggest t~at the
Tog dure is insensitive to changes m the target behavior fio
measurement proce fi hi . r
Solution is to address the reasons or t s msensitivity If some reason. 0 ne b · f ·
that Observation periods were too ne or not scheduled the pro bl em was . .
. t 1 they could be adJ’usted. Another solution is to measure other appropna e y, inti · ·
. . t’t’es which might provide a more ormative picture of what d1IDens1on quan 1 1 ,
is happening with the behavior. . .
As an example of these situations, consider data from partial mterval record-
ing using 5-minute intervals that showed co~sistently hi~ percentages of
scored intervals. It could be that the relatively long mtervals result in
most being scored as containing at least some of the target behavior. Interval
recording procedures do not measure dimensional quantities, however, so
the researcher might worry that there is interesting variation being missed in
quantities such as count, duration, or frequency. Again, steady-state data might
not always mean that all aspects of responding are stable. Such data must be
examined in light of what is being measured and how it is being observed.
Reveals the Influence of Conditions. The steady-state strategy is espe
cially valuable in revealing what is going on with the target behavior as it
accumulates contact with a condition. It is typical that when a behavior is
exposed to a new condition the behavior changes in some way. Although the
new condition may have some immediate impact, the still recent experience
with the previous condition may still be having some effect. In other words,
the data often show a mixture of influences at the beginning of a new phase
that reflects a transition in control from the previous condition to the new
condition. Although this transition is sometimes a particular focus in some
research projects, more often it is merely a nuisanc;:e because it complicates
seeing a clear picture of the effects of the new condition alone.
As. t~e data show an end to the transition in responding that started when
conditions changed, it is tempting to assume that the full effects of the new
con~ition are finally evident. The data may now represent a level of responding
that is ~otably higher or lower than in the previous condition. Although this
~hange m the level of responding may show the impact of the current condi
~100.’ the steady-state strategy asks for evidence that the new level of respond-
mg 1s durable That · th h 1 · is, e researc er needs to be sure that the apparently stab e
responding will t ·
con mue as long as the condition is in effect. If responding
were to eventually ch · . .
ange 1n some way, it would be important to include this h c ange as part of the ef£ t f h . . .
graduall d ec s
O t e condition. For instance responding nught
Y ecrease when an · t · . ‘ . d
exposure, this low level m en:-entio~ ts started. However, with cont1.11ue
level that existed in the of ;;5pondmg ~t.ght gradually climb back to the higher
capture all of th h P vious condition. The steady-state strategy helps to
ese c anges that might be characteristic of the condition.
oY srAtES
s’l’Et
evaluates Experimental Cont l. 199
P ct· · ro Measu ·
r the same con 1tton can also al nng a behav1· uJl de ert the · . or repeated!
us variables. Remember that any f: mvesttgator to the r 1 f Y oe O . actors th t o e o extra-
iOdependent vanable are extraneous to ex e/ are n~t explicitly part of the
described, such extraneous factors may hp imental mterests. As chapt 8
. d e unrelat d er
preparation an may occur unsystematically a fir ~ . to the experimental
ever, they may also b~ at~ached to the general ~ircu e drill m a preschool). How-
therefore have contmumg or systemat· f mstances of a study and m . ic e fects (inti ay
for a study con d ucted m the workplace) Th uence from coworkers
independent variable itself and there£ · ey can even be attached to the
. ore come and g . .
withdrawn. (In struct1ons associated w·th O as 1t 1s presented and 1
example of this last category.) treatment procedures are a good
The steady-state strategy creates a g d
of unsystematic extraneous factors th ~o .;::Portunity to detect the influence
As we will see, instability in the data ca ~ t o~cur at some point in a phase.
its sources must usually be guessed fan e rbelatt~ely easy to identify, although
. . rom o serv10g wh t · ·
sessions. It 1s usually more challenging t . d . a is go10g on during
O 1
factors that are consistent throughout a e~t~ the infl_u~nce of extraneous
0 1 1
well, their contribution may be missed ~ ~ ~o_n. If their unpact is stable as
session to session however chan . · e~ unpact ebbs and flows from
‘ , ges 10 respond10g may hint t h ·
under an otherwise stable set of conditions. a t eir presence
The ste~dy-~tate strategy can help to identify unstable responding, but the
real q~~st10~ ts what the researcher is able to do about excessive variability
?nee tt 1s evident. Studies differ from one another in how carefully extraneous
influences must be managed. Some experimental questions and procedures
require a high level of control, perhaps even a laboratory setting. However,
even studies conducted in messy, real-world settings often require some
management of extraneous influences. Whatever the requirements of an
individual study, the level of stability in responding reflected in the data is a
measure of the level of experimental control that the investigator has achieved.
Facilitates Experimental Comparisons. As we will see in more detail
in the upcoming chapters on experimental design, the steady-state strategy
provides the foundation for making comparisons between the effects of con
trol and intervention conditions. Drawing conclusions about the effects of an
intervention condition that have a good chance of being true, and therefore
dependable, depends on how well the effects of both control and intervention
conditions can be described and understood.
Efforts to establish stable responding under each condition help the investi-
gator to do this in rwo key ways. First, repeatedly measuring responding under
each condition helps to identify both the initial and final patterns of respond
ing · h hase. second, these data also encourage efforts to manage
10 eac p h l . . . h . infl d h b ·ables which e ps to rmrunuze t e1r uence an t ere y
raneous van , .. ext ‘&’. cts of each condition. These two outcomes of the steady-state
c I ar ifi es t h e e f.ie d” . . h b f’t:. f h d’ . h investigator to 1stmgrus etween e .1ects o t e con 1t1ons
1 e f other factors. The resulting comparison is therefore more strategy h~&’.p t
and the ef:iects o
9. STEADY STATES AND TRANsrnoNs
200
likely to be an accurate description of the effects of t~e intervention. ‘fhis also
means that the .findings have a good chance of holdmg up when others Use
them in some way.
As an example, let us suppose that a researcher is conducting a study in
developmental center looking at t~e _P?ssibili~ that a certain p~chotropi~
drug may make it more difficult for mdividuals with mental retardation to learn
new skills. Each individual’s performance, ~easured rep~atedl~ under control
and drug conditions, will be partly a function of the basic testmg procedures
used throughout both conditions. In addition, performance under the experi
mental condition should reflect any influence of the drug. However, what if
there are events going on in the participant’s daily living conditions that
vary from one day to another and that might affect their ~~rform~nce in daily
testing sessions? One individual may be moved from one livmg urut to another
a kind of disruption that often has broad effects on behavior. Another may g~
home on some weekends and behave differently on Mondays as a result. Still
another may have health problems and may not be feeling well on some days.
Any effects of these extraneous factors may show up as variations in
acquisition performance from one session to another. If the researcher ignores
these variations and concludes that the difference in learning performance
between the two conditions is due solely to the drug, this finding may not hold
up very well for other researchers or practitioners. On the other hand, if
the variations in responding within each condition are used to identify the
contribution of the extraneous factors so that they can be better managed, it
will be easier to identify the influence of the drug.
Identification
Trends. One of the challenges of the steady-state strategy is recognizing
‘:hen the data show stable responding and when they do not. There are par
ticular features of variability in a series of data points that often bear on this
decisi~n. One pa~tern of variability is called a trend. A trend may be defined as
a relatively consistent change in a single direction. Although there are some
excepti?ns, steady-state data do not show strong or consistent trends in either a
decreasmg or an increasing direction.
It is not necessarily easy t O t 11 if h d . . d e t e ata m a phase are generally trending
up~ar or downward. There are endless ways in which a sequence of data
~~::~;t:s s~w tren~s. The graphed data sets in Figure 9.1 shows some
. · e data m both Panels A and B show a slightly increasing but
consistent trend How h
· ever, t e greater range of the values in Panel B might
mas k th e f: act that the slop f h . · O Panel A. . e t e trend m these two data sets is the same as i.11
201 STEADY STATES
BOX 9.2
One Data Point at a
Time
A trick that can help you apprec· t ti . ia e 1e imp t
repeatedly under each condition . or ance of measuring respond” . . is to look at th d . mg
a a tune Pia . 1e mvesttgator sees them-one value t . e ata m the same way ti
the first few data points on a graph Th · ce a piece of paper over all but
1 . . · en slowly sl’d h
uncovermg successive values one at t· e t e paper to the right . a une. ,
If you do tlus for the graphs in Fi 9 1 .
decide when the data show a trend.~ p· ‘ you will se~ how difficult it can be to
9 2
to determine when the data are stabl ;g, · • you will see that it is challenging
valuable it is to see additional data p . e. n most cases, this exercise shows how
01nts.
A B
(lJ ….
:::::,
“‘ ca
“‘ \
Cl)
E
Cl)
VI
C
0
a.
VI
/
Cl)
a::
C
D
F
Time
FIG. 9.1. Graphed data sets showing different trend patterns.
In Panel C, most of the data points fall in the middle range of the vertical axis.
fhe existence of a downward trend results from the fact that there are four
Iigh values in the first half of the data set and four low values in the second
1alf. TIIis type of trend is also shown in Panel D. Here, although most points fall
9 . STEADY STATES AND TRANSITIONS
202 . .
. . number of lower values begin appearing in
in the middle of the vertical axi~;h of these graphs, the trends result from the
the latter half of the data set;~ oints in the set.
pattern of only a few of the a a Pall d “local” trends because they are embed
Panels E and F sho’: what are c a::..erns of responding. In Panel E, the values
ded in otherwise relatively stable P . trends J’ust as the values marked a in
d b eal brief decreasmg , h
marked a an rev . . trend. It is tempting to ignore sue local trends
Panel F show a sharp mcreasm!nd are surrounded by more stable data. This
because they are temporary if h changes happened to occur at the begin.
might not be a pro?~em,. but ~ui easy to mistake them for the effects of the
ning of a new condition it wou 1 e
new condition. d · · gest that there m
Trends that appear under a constant set of con itions sug . h ay
~ k As we have already pointed out, if t e researcher be extraneous 1actors at wor . . .
· full · the conditions defining a control or mtervention 1s success y managmg . . .
phase, the participant’s experiences should be very similar from session. to
session. Under such conditions, it is reasonable to assume th~t respond~g
would be relatively stable. If a consistent increase or decrease tn respondmg
occurs there must be something producing this change. If the trend appears at
the be~g of a condition, it may be that it represents the initial effects of the
condition. However, if it occurs after the phase is well underway, it may mean
that extraneous factors are at work.
In sum, there are at least three reasons for worrying about trends that have
no obvious explanation. First, a trend means that some variables are influencing
behavior in significant ways, which might interfere with seeing the impact of
either the control condition or the intervention condition. Second, when the
researcher is unaware of what is causing a trend, it is not clear how to more
effectively control the environment to reduce the impact of these factors.
Third, trends make it more difficult to determine the effects of an intervention.
If the effects of either of the two conditions being compared are unclear, this
distorts their comparison. The result can be that the effect of the intervention
condition is seen as greater or smaller than it really is.
Finally, there are circumstances in which trends may be considered stable
patte~s of responding .. This would be the case when procedures produce a
repeatmg pattern of bnef trends throughout both control and intervention
c?~ditions. The data in Figure 9.2 showing measures of a student’s correct
digits on multiplication problems illustrate this kind of pattern Oohnson &
La~ng, 19:2). The data show increasing trends within each type of problem
ass~gned m each of the four conditions. When each type of problem was
a:igned, performance improved somewhat over successive measures. Because
~ s same local trend pattern is clear in each phase the repeated trends do not
mterfere with making d . ‘
soun comparisons between the multiplication fact h P ases and the alternating ph · ·
putations In thi ases assigrung double digit multiplication com-
of about .70 d.s _case, the data show an initial multiplication fact performance
student’s flu ig1t~ per minute. When the teacher attempted to build the
ency m complex multiplic r s
poor (about 15 di it . a ion computations, performance wa
g s per mmute). A successful effort to build multiplication
10
203
STEADY STATES
1000 .. .__ __
——;—-:—–
100 -~
r:RP t9:P [DJjJ q5tJ ¥3 0 D ~ [llJOLr
~~
L_
ltp I c9° rn CI
-6d
~
(1) 1 –
C.
,tJ
C:
::s
u
0
o.,r -~~~;–~2-~=–~—-Multiplication
facts D~l;lble Back to Back to double
digit. . multipli- d” ·
mult1ph- cation facts ig1t multiplication
cation computation
0.01 ,—————-~c~o~m~p~ut~at~io~n!……. ________________ ~
~ Correct Digits I Instructional Phase Change
0.001 ~ –:~·;;:–:!::~:-:~~–,—-r–,r-~-.–“””‘T'”—
0 7 14 i1 2s 35 42 49 56 63 10 11 84 9″1 9″8
Successive calendar days
FIG. 9.2. Data showing trends as a pattern of stable responding.
fact skills in the third phase then led to improving performance on the more
difficult problems in the final phase.
Range. Another feature of variability that is important in identifying steady
states is range. Range is defined by the highest and lowest values in a data set. It
is particularly easy to determine the range of a set of values when data are
plotted on a graph. Figure 9.3 shows some different patterns of variation in
range that can influence decisions about steady states.
The data sets in Panels A and B each show a fairly consistent range fr~m
beginning to end Whether the range of either data set would be acceptable_ or
an investigator ~ould depend on the actual values that tht~’:1:i!:U~~
represent, as well as various features of the study. For examp e, 1 bem· g
the . e the response c ass
experimental question, the relevant literatur ‘ d. occurs and the
tneas . d hich respon mg ,
. ured, the general preparation un er w f t ble van· ability. For
tnde ~ . dard o accep a ·
th ~. Pendent variable all contnbute to a stan d on the performance of
tsc reasons, a laboratory study of the effects of a rug
204
9. STEADY STATES AND TRANsrr10Ns
B
A
OJ
‘-
::J
V\
co
OJ
E
OJ
V\
0
C D C a.
V\
OJ
0::
E
Time
FIG. 9.3. Data showing different patterns of variability in range.
rats under particular schedules of reinforcement might require less variable
data than a field study of the effects of an intervention on the behavior of
individuals living in a developmental center.
Although most of the data points in Panel C fall in the same range, five values
are markedly lower. These lower values seem consistent and are not apparently
occurring more often. Nevertheless, they suggest that some influence is at
work for some sessions that is different from what is going on in most other
sessions. If the researcher is aware of why responding is lower on some days, it
might be acceptable to ignore these values. However, if they have no obvious
explanation, it should be worrisome that they might become more frequent,
possibly making it more difficult to see the effects of an intervention condition.
Panel D presents a similar problem. In this case however. there are more
‘ ‘ . than a few data points that exceed the range otherwise defined by the majonty
of the values. This kind of variation in range from one local group of values
to the next does not seem like a very good basis for predicting the outcome of
future measurements. Again, there are two problems: (a) we should wond~r
what extraneous factors are producing these changes in local range; and Cb) it
would not be clear what level of responding would be used to represent the
effects of this phase. If the next phase was started at this point, the net effect of
STEADY STATES
205
these problems is that conclusions about the ffi t f
be uncertain and might not hold up for others.e ec o an intervention would
The data points in Panel E represent a particularly troublin ·t f
Although we could determine the range of the entire data set ·t gldsb1 ua ion.
· gful t ~ h . , 1 wou e more
roearun o 1.ocus on w at 1s happening with the local ran It ·
. all b · k . ge. 1s genera 11 y
fairly sm , ut 1t eeps movmg around This pattern of l al . . · oc range va 1 ues
provides a poor basts for describing the effects of the present co d·t· In
uld ak . diffi n 1 ion.
tum, t hi s wo ~- e tt cult to determine by comparison the effects of a
subsequent condition.
Cycles. A cycle is a locally complex pattern of variability that like trends
can ~ometimes be conside~ed. ~ stable pattern of responding. A ~cle is a re~
peatmg patt~m of local_ v~ability that often involves sequences of increasing
and decreasmg trends (m either order). The highest and lowest values in each
cycle define its range, and the time taken from beginning to end is called its
period. The periodicity of the cycles may be either regular or unpredictable.
Furthermore, the cycles may appear consistently or on an irregular basis.
Cycles may be considered stable or unstable in their different features
‘
including their form, the level of responding involved, and their frequency of
occurrence.
Cyclic patterns of variation in behavior are not uncommon. However,
identifying them requires good environmental control, not to mention careful
measurement. They are therefore more likely to be clearly detected under
laboratory than field conditions. However, a weekly pattern of cyclic respond
ing is sometimes found in applied research and practice. Regularities in
environments and activities from one week to another may show up in fairly
consistent changes in responding throughout each week. A consumer living in
a group home who goes home to visit her family each weekend may typically
behave differently on Mondays or Fridays, for example.
Figure 9 .4 shows three examples of cyclic patterns of variability. In Panel
A,
the cycles are regular in their form and frequency of occurrence within each
Phase. The fact that a number of cycles occur under each condition helps to
Provide a good basis for comparing differences in responding under the two
conditions. Panel B shows cyclic variations that are less regular. They vary in
their form and frequency of occurrence. Nevertheless, the cyclic patterns show
0 ne general level of responding throughout the first phase and a different
but consistent level of responding in the second phase. For this reason,
the investigator is in a good position to compare responding under the two
conditions.
The data in Panel c show the greatest risk associated with cyclic data. In this
case, the cyclic character of the data is not recognized, and the phase changes
206
9. STEADY STATES AND TRANsrnoNs
A
Treatment
Baseline
(1)
L..
::,
VI
co
(1)
E
(1)
VI
C
0
C.
V)
(1)
0::::
Baseline
B
Treatment
C Baseline Treatment Baseline
Time
FIG. 9.4. Stylized data showing various cyclic patterns of data.
happen to correspond to the local trends defining the cycle. With the
advantage of hindsight, it is easy to see that the decrease in responding under
the treatment condition might be at least partly due to the factors causing the
cycle, rather than the treatment condition alone. This risk is another reason to
avoid changing conditions when the data show a clear trend, especially when
the expected change in responding is in the opposite direction.
Criteria
Uses. Deciding when steady-state responding has been attained is such a
frequent challenge that some informal criteria or rules have evolved to help
researchers. There is certainly no denying the importance of the decision. It is
not just about whether responding is stable. It is about whether to continue the
present condition unchanged, modify the environment by managing more
variables to improve stability, or end the condition and begin the next phase. It
is also an assessment about whether the effects of the present condition ha~e
been fully determined and represent its typical influence on responding. It 15
207 s’fEADY STATES
therefore a decision about whether there is a sound basis for comparing one
oodition with another condition. Of course, this judgment is the same for both
~ontrol (baseline) and independent variable (treatment) conditions.
The function of a decision rule is not necessarily to force identification of a
steady state as much as it is to help the researcher to focus on some important
considerations. Remember that the decision about when stable responding
has been achieved should be partly guided by the nature of the question,
the procedures used in the study, and the standards evident in the literature.
Sidman (1960) summarized the task nicely:
‘fhe utility of data will depend not on whether ultimate stability has been
achieved, but rather on the reliability and validity of the criterion. That is to say,
does the criterion select a reproducible and generalizable state of behavior? If it
does, experimental manipulation of steady states, as defined by the criterion, will
yield data that are orderly and generalizable to other situations. If the steady-state
criterion is inadequate, failures to reproduce and to replicate systematically the
experimental findings will reveal this fact. (pp. 257-258)
StatisticaL One kind of criterion involves a statistical description of
variability. This approach usually specifies a limited amount of variability that
will be permitted over a certain number of data points. For example, such rules
might describe the maximum range for a number of sessions: “No data point
may deviate by more than 5% from the median of the last five sessions.” They
might instead impose a limit on the difference between the means or ranges
of two successive series of sessions: “The means of two consecutive sets of
10 data points may differ by less than 10% of the total range.” The possible
specifications of this type of rule are nearly endless.
Although the mathematical precision of this type of criterion might seem
reassuring, it has some risks. Consider that the degree of variability in the data
may well change from one condition to the next. It is not uncommon, for
instance, for an intervention condition to produce less variability than a control
condition. In this situation, it is possible that a fixed criterion would select good
stability in one phase but would be immediately met in another phase. This
might not provide sufficient exposure to the second set of conditions.
In other words, suppose that a baseline condition with fairly variable data
was followed by a treatment condition that generated much less variable
responding. A statistical decision rule might lead to a decision to terminate the
treatment condition before its effects on responding were fully developed. This
possibility means that researchers who decide to use a statistical criterion of
stability should not do so blindly. They need to remain alert to the need to
adjust the criterion if the data warrant.
Graphical. The most popular approach to stability criteria is based on
ongoing visual inspection of graphically displayed data. This preference avoids
the risky commitments of statistical rules in favor of thoughtful judgments.
These intentionally subjective judgments involve carefully studying the evolving
A y
C
y
d
y
e
A,
Intervention
4r
f
‘,i
g
208 9. STEADY STATES AND TRANS
rr10Ns
graphical picture of variability i~ ~ phase ~s ~a.ch . new ~ata point is addect
Researchers look at the characteristics of variability m their data and Wait . ·
bil·ty h b . Until their professional history tells them w h en sta i as een attained. We tn.igh
call this type of criterion the Supreme Court standard: “I can’t tell yo . t
. h I ‘t ,,1 u in
advance what it is, but I’ll know it w en see i .
It would be a mistake to view this kind of criterion as less demanding th
· d t· 1 · · an the statistical approach just because it oes no mvo ve an a pnon mathem
hi 1 at-
ical stateinent. For well-trained researc h ers, t hi s grap ca approach is mo
. ‘ld re
sophisticated than a quantitative ~le, an d it. may yie . a more meaningful
picture of stable responding. Investigators using a graphical standard should
be able to specify each aspect of the data they are considering and why those
features might be important.
Figure 9. 5 shows data in baseline and intervention phases that illustrate this
approach to steady-state criteria. (We again suggest putting a piece of paper
over the graph and uncovering the data points in chronological sequence one
at a time.) The early data points in the baseline phase labeled a show a sharp
decreasing trend, and there should be no temptation to describe them as stable.
As the data labeled b are fully revealed, we see that this was a wise decision
because an increasing trend becomes ·unmistakable. Additional data points
(labeled c) tend to mostly fall in the upper end of the overall range. However,
we keep finding an occasional low value, which should make us a bit con
cerned about the influence of extraneous factors. This might even prompt
an effort to identify and control the suspected factors. Finally, we see more ·
data points (labeled d) in the upper part of the range, though with no lower
values intruding. Perhaps, most importantly, these values show no further
evidence of the trend. We might find the data labeled d an adequate steady state
of responding.
Successive calendar days
FIG. 9.5. Data illustrating the complexity of decisions required by graphical
steady-state criteria.
d th · · n in the 1 Base on e well-known statement of Justice Potter Stewart in his concurring optn.1°
Supreme Court’s pornography decision in Jacobellis v. Ohio, 1964.
Q)
I…
:::::s
V,
ca
Q)
E
Q)
V,
C
0
0..
V,
Q)
ex::
Baseline
STEADY STATES
. 209
Th e mtervention ph
(labeled e), which “bottase begins With a relatively rapid decreasing trend
oms out” t 1
stable portion of the bas lin a a ower level than was seen under the
labeled/ show an increas·e e phase (d). In contrast, the accumulating data
. mg trend How
g, it becomes clear that the t d · ever, as we see the data points labeled
. ren has “topp d ,, d . relatively stable. In sum th e out an responding has become
‘ e successive val · h h trends that eventually end . . ues m eac P ase show some local
it is easy to see that neith
10
a hsenes of relatively stable values. In hindsight
er p ase showed t bl di ,
ten or so values were obtain d E . s a e respon ng until the last
data in each phase show e · ven without the benefit of hindsight the
encourage the investigator etnough ~vidence of a trend or outlying valu~s to
o contmue the phas b·t 1 d 1 k clearer picture of stability. e a i onger an oo for a
Nondata. Investigators often face limits on the . .
complete a project Th limi . . amount of trme available to
” ·. ~ tmg factors mtght be participants who will only be
ava il a bl e i.or a certam trme restricti . , ons on access to a research setting, pressure
to mo:e qwckly ~o a treatment condition that will resolve clinical problems, or
fin~ci~ constramts. In such situations, it may be necessary to set advance
restn~tions on t~e number of sessions for each of the planned phases of the
expe?111ent. For mstance, this might mean allotting 1 O days for a control or
baseline phase, 3 weeks for an intervention phase, and so on.
In this approach to steady-state criteria, each phase therefore lasts for
a predetermined number of sessions. We may call this a nondata criterion
because it is based on considerations that have little to do with the nature of
the data obtained under each condition. This means that each phase would be
terminated without regard for what the data reveal about responding. Even
though responding might be unstable throughout a phase, the next condition
would be implemented at a certain point because the study’s schedule requires
it. This kind of decision-making might result in a weak basis for comparing the
effects of control and intervention conditions.
Although nondata criteria for deciding when to change phases are risky,
there is no denying that researchers might sometimes feel it necessary to limit
the length of phases. The ideal solution to this situation is to_ try to confront the
factors that are pressuring the investigator to compronuse sou~d m~thod
ological standards. If it is not feasible to resolve these pressures, the mvestigator
may need to consider whether the planned study can be successfully con-
ducted under the presenting circumstances.
Establishing Stable Responding
It is easy for discussions of the steady-state strategy to c~eate th~ impres~:n
that obtaining stable responding is largely a matter of bemg patient. It ~ t
seem th . h t do is continue a phase for long enoug so
th at all an experunenter as o . cumulate Sometimes this passive
at, eventually, acceptably stable data will ac t d ;ariability may be due to
approa h d . d d k F example unwan e th ~ c oes m ee wor . o~ . ” , . off’ or the effects of the present
c effects of the previous condition wearing
210 9 . STEADY STATES AND TRAN
SJl’10Ns
condition becoming fully established. In tWs situation, simply contin .
phase until these transitions are complet~ ~~y yield satisfactory stability~tng a
on the other hand, the excessive variability may result from poor cont
over the procedures in a phase or from uncontrolled extraneous variab ro1
Merely continuing the phase will not often resolve such problems. They ~~s.
probably require specific efforts to improve experimental control. Continu· ill
data collection will then assess whether these efforts were successful in d ltlg
ing a clearer picture of responding. raw.
Establishlng stable responding can sometimes be challenging and r
consuming. When tWs is the case, it is important to avoid the temptatio un.e.
manipulate data in ways that imply a level of stability that has not actually bn to
achieved. The three panels in Figure 9.6 show how combining data een
over
~ (1)
.c
– I…
:J
+,J
c: ro
V)
0 (1)
EE
(1) (1)
O’> A
ro c:
V)
I… 0
(1) 0.
> V)
1 2 (1) – :J V) ~E 1 2 3 4 5 6 7 8 y C
10 20 30 40 50 60 Successive days
FIG. 9.6. Graphs showing the effects of comb. . . . f 211
TRANSITIONS different tutits of . gra~hs are based on the ects the resulting icture . . . … · Si1nil in o monthly a .u.1 TRANSIDONS
Transition States When responding is unstable d . . . ges m responding · di · . . ‘ ct· . c e a transition state In transition ta Given the emphasis on steady states, it ~ght seem that researchers might distraction. look like is the central focus of behavioral research. It is useful to obtain stable emerges. 212
9. STEADY STATES AND TRANsn10Ns Q) E C a::
“‘ C D FIG. 9.7. Schematic representation showing different types of tranSitions that might result from changing from one condition to another. In :: c. • • Th transition not start 1or some time after the second condition begms. e urs 213 rRANSITIONS
Q) :::J E 0:::
Successive days FIG. 9.8. Data showing a transition state occurring well after the imple Transition states do not only follow changes in the environment initiated Figure 9.8 shows an example of this situation. Responding increases Transitory States We have so far considered transitions from one steady state of responding that l 9. STEADY STATES AND TRAN I Transito;-;~;~:-;;~;;r~-~~~e:ponding involving . a de~~~~-1 D. t· guishing between transition states and transitory states is very un· 1s m . d. d Por. 1 tant. In one case (transition states), changes m respon. mg ea to a different Here is one way this can happen. Although transitory states often result from In this situation, it would be critical to determine whether the initial change (I) ro E C a:: Transition state –.Jr–
a b C d
Time FIG. 9.9. Schematic representation of transition and transitory states. 215 r!lA
rransition resulted in a durable or only a temporary change in behavior. In identification
d ntifying transition and transitory states is a matter of identifying the steady sponding is measured during a transition, the more precisely the transition’s Figure 9.10 illustrates this point. The three panels show the same transition, ll y Q) B co Q) E C: a:: FIG. 9.10. Data showing the results of different frequencies of measurement
during a transition state.
y Cl) ::J re E C: a:::
9. STEADY STATES AND TRANsn10Ns f tlle transition. However, this picture fails to Ioc t t:. • the oint at which stable responding begins to reappear. e at~ m Panel c remedy t 1us s ortcommg . . . d . nt Making Phase Change Decisions we have already emphasized the general risks associated with .m~g a deci Panel A shows an increasing trend in the first phase followed by a con Time FIG. 9.11. Data from control and int · TRANSITIONS 217
Box 9.3 Ould Each Ph By now, you should . that can be s no genera.I answer . e right question. You should also satisfactory stability. h ere is any rule, it is only th a: number of sessions e existing literature th arc project. For each study the e aractenstics of control . measurement procedures selected meer stable resp din H d o not lessen the value of the t d on g. owever, these challenges Finally, it is important to un:~stand th b . . .ffi t f h gy . . atrun~ ~ sample of responding that fully represents the have tried to minimize their influence. If they are unknown, there would be no Panel B shows a similar increasing trend in the first phase but a contrasting ‘ · · can change behavior but what re al question is not whether an mtervention actors). The problem here is actually t e sa;; eous factors are operating 9, STEADY STATES AND TRAN does not know what responding in the 8 fir t phase The researc h er “b . f h econct phase would have 1 oo e neous
factors. h f Panel C an initial period of relatively stable responct· is followed by a clear downward trend. Respon mg t en mcreases .under the secon d p h ase, t e nrun . uld st p h ase. A s m . bl d . h fir ce Finally, Panel D shows three phases containing relatively variable data that Of course, all four examples suffer from the fact that stable responding was Turnitin Report Formatting Title Page Citation Outline
Successive months
~ ‘
~
(1) ro
(1) (1)
(1) (1)
O’> V) B ro C
‘- 0
(1) 0.
>
Successive weeks
time on displayed variability. uung data over different uruts o
tune af£
penod. When these Val same hYPothetical ~ of variability. All three
two data points a .. e . ues are comb1t”led. t ues collected over a 2-month
aged on a weekly bas. ar, suggesting good stabili w:erages (Panel A) the
followed by a down~s rnel B) we can see that tZ· ~ en the data are aver-
(Panel C) the weekly ~n:~~- Whe_n the data are :p~:;d~:~’:;~d tre~d
~so reveals a weekly cycle in e a~am evident. However, the daily W:e s:it
higher than the other da which Monday and Frida al t . ys of the Y v ues tend to be
can appreciate that the di . week. Seeing the data in Pan 1 B . sp ay in p e s or C we
o sta ty. 1 ane 1 A would be a mislead’ mg d escr1pt1on · ‘ . f bill
being able to identify chan ‘ w~ escnbe it as bemg in transition. Of course
changes from periods of stable respondin . reqmres stmgwshing these
tion. One kind of transition is all d g go”?~ on before and after the transi-
respon mg ts changing from one steady state to a differen~ steady state. s tes,
not be especially interested in transition states. After all, they often result from
switching from one condition to the next. When this is the case, they reflect an
expected, though usually temporary, mixture of influences from the old and
new conditions. Because the researcher is typically interested in identifying the
effects of each condition alone, this transitional interlude would seem to be a
In fact, understanding what makes behavior change and what these changes
responding because it tells us that a transition is completed. When responding
stabilizes following a switch from one condition to another, for instance, it
tn~s that we may now have a picture of the behavioral changes associated
\V1th that switch. What we are really interested in, however, is what kind of
changes in responding were produced by initiating a ?ew co~~tion. The
steady state is an endpoint for those changes in responding, but 1t 1s not the
Only effect of switching conditions. Toe effects of the new condition include
the nature of the transition just as much as the steady state that eventually
Figure 9. 7 shows a schematic representation of different kinds of tranSitions
A
~ B
::J
“‘ ro
Q)
Q)
0
C.
“‘ Q)
Successive days
resulting from introducing a new condition.
example, steady-state responding in the first and second conditions is ~he .5 of
thereby highlighting differences in transitional responding at the beginllUIJres
the second phase. In Panel A, the transition between the two steady _s the
begins as soon as the second condition starts and quickly terminates 111 the
new steady state. In contrast, Panel B shows a transitional pattern betwee~ es
two steady states that is relatively gradual. Panel c shows a transition that O iO
Panel D is unlike the others in that a temporary increase in responding occ the
before the decrease that then leads to the lower steady state. Depending 011 ess
features of the study, these differences in transitional responding may be 11~ 1 . o
important than the fact that changing from the first to the second conditt 0
eventually resulted in a decrease in responding.
~
VI
co
Q)
Q)
VI
C:
0
a.
VI
Q)
mentation of a new condition.
by the experimenter, of course. Extraneous variables can just as easily lead
to behavioral changes at any time during a phase that terminate in a different
level of responding. Such transitions can be mistaken for the effects of treat
ment conditions, so it is important to describe them fully. If an unanticipated
transition state appears well after the condition has begun, for example, it may
be wise to suspect an extraneous influence.
immediately following the start of the second phase, suggesting that in this
case there may not be a gradual transition initiated by switching conditions.
However, well after the new phase is underway, responding abruptly increases
further and eventually levels off. This transition state is a problem because it is
now unclear whether it is a delayed effect of exposure to the new condition or
the result of some extraneous factor. Because we cannot answer this question,
we also do not know whether the real effect of the condition is reflected by the
level of responding at the beginning or at the end of the second phase. One
way to resolve this dilemma is to identify and hold constant or eliminate the
suspected extraneous factor and see how responding changes.
eventually result in a different steady state. Transitional patterns may instead
end in a return to the original level or pattern of responding, however. These
are called transitory states.
214 S11’10Ns
f from a steady state that ends in a return to the same steady state.
L_ .. ~. -, = ••.• — ·” ·”—·-,- ~-· –” ··· ·–· .~- ., -·- .. ·–··· ····· ·· – __ __ _ …… ···–···-··-·..;.., …. – ~ …… -· .,.-” – ····”‘-“··”·—-~·–···- – ·-~···-· ····-·–···—-·’— .. ..1
level of responding than before. In the other case (~rans1tory states), changes.
responding return to the original level of respondmg. If one was mistaken ri”1
the other, it might result in misunderstanding the effects of an intervention. or
extraneous factors that occur during a phase, they can also be the outcome of
treatment conditions. In other words, the investigator may expect an inter
vention not only to change the participant’s behavior but to maintain that
new level of responding as long as the condition is present. However, it may
be that the impact of the intervention is only temporary. An initial change in
responding may dissipate, eventually leaving responding at the level that
existed before the condition was introduced. This kind of change in respond
ing would be a transitory state.
in responding was going to lead to a new steady state or back to the original
one. If an intervention phase only lasted long enough to capture the initial
change, we would not know whether the effect of the intervention was
durable or temporary-a very important distinction. Figure 9.9 shows a
schematic representation of this situation, as well as what can be done to avoid
this confusion. The initial steady states (from point a to point b) and transitions
(from point b to point c) are the same for both functions. At point c, the
experimenter cannot tell which type of transition is happening. In order to find
out, the phase must be continued until at least point d, at which time we can
see that stable responding has developed in both cases, though at different
levels. If the condition was terminated at point c, we would not know whether
r….
::,
V)
(I)
(I)
V,
0
0.
V)
(I)
r..ss1rr0Ns
t11e r words, any conclusion about the effect of this condition might be wrong
otl~e the experimenter would not know it. This is why it is so important to
aJl ,,,n a period of stable responding under each condition.
obt’1PA
1 ~es preceding and following the transition. The frequency with which
sta asurement occurs can affect how well an investigator can describe the
:sition and distinguish it from the surrounding steady states. The more often
~:undaries can be described.
but the features of the transition depend on how often measurements were
made. Toe infrequent measures shown in Panel A give a false impression that
responding simply jumped to the new steady state by the second data point in
the new condition. The increased frequency of measurement in Panel B begins
)f
r.
lt
A
::,
~
V,
Q)
V,
0
C.
V,
Q)
C
Time
I,..
V,
Cl)
Cl)
V,
0 C D C.
V, I I Cl)
216
to capture the tn1e 1oun
o . Th d . a e
p . h . because the frequency of measurement 1s sufficie
to provide a full picture of the transition and to identify its en pomts.
sion to change from one condition to another when respondmg ts unstable.
There are some special cases of this scenario that warrant discussion. The
graphs in Figure 9 .11 represent situations in which there may be some temp
tation to change phases even though stable responding has not been obtained.
It may again be useful to look at these graphs one data point at a time by sliding
a piece of paper from left to right.
tinuation of this trend in the second phase. If the researcher anticipated
that the second condition would lead to an increase in responding, it might
be tempting to conclude that the variables defining this condition were
responsible for the increasing trend. Although a new condition might initially
produce a transition like this, we should be concerned that this trend started
under the first conrlition. Because the researcher is trying to hold the features
of each condition constant throughout each phase, we should assume that
this initial trend may reflect uncontrolled extraneous factors. These extraneous
factors are likely to be unknown, otherwise the researcher would presumably
of instability. ervention phases involving different types
HowLongSh
ase last?
reabze th t hi kn ow that there i a t s is not th .
correct If th . m terms of a cert ·
depends on the . as been obtained. The lev l at p .a~es should last until
standards in th u~~ue features of each rese eh of s~ability that is necessary
!~~es: the re~p?nse class, the, de;a~:t:e of the experimental questiod, the
should be considered and mtervention conditions are all f: t h ‘ . · ac ors t at
. No~ce tha~ this list does not include th . .
mg. It ts certainly true that some . e difficulty of obtaming stable respond
the researcher’s ability to eng· projects provide insurmountable limitations on
risks of proceeding without th/ ea y-state strategy. Neither do they reduce the
merely a “ticket” to the next phase like at ~ tammg stable responding is not
to the home plate in order to get a hom touc~ng .each of the bases on your way
the larger strate of obt . . e run. ettmg stable responding is part of
e ec. s. o eac condition. This information then allows comparisons between
condit10ns that have a good chance of being both reliable and general.
basis for assuming that these influences stopped when the second phase was
started. This means that they could be responsible for the continuing trend in
the second phase, which raises doubts about the contribution of the condition
itself. In sum, the data in Panel A cannot support a conclusion that the
increased responding in the second phase is due to the variables defining this
condition.
downward trend in the second phase. This change in responding associated
With the second condition might seem to allow a clear basis for attributing
the change to the new condition. Although it might b~ reasonabl~ to assume
that the contrasting pattern of responding is related to tmplementmg the new
condition this is not exactly what the researcher should be trying .to learn. The
·t If (in the absence of extraneous kin d of effects the condition produces b Y 1 se h f p 1 A f: h e as in t e case o ane .
~ecause the researcher does not kn~w wi:~:r:: in responding, there is no
n the first condition that are producmg t~ h second condition. The proper
~tason to assume that they disappeare~ u:i \ ;hey may represent the effects of
Jntcrprctation of the second-phase data 18 t a. bles producing the trend in the
the condition itself plus the extraneous varia
218 SIT10Ns
s · k d like without the contri ution o t ese extra
In the first p ase o , d” h . tng
. ating at about the same level as observed early m the fir
. the case of Panels A and B, this mcrease co reflect the influen
of the unknown variables producing the vana e a_ta m t e st phase, the
impact of the new condition by itself, ?r an interaction of ~hes~ two factors.
The data provide no basis for concludmg that one alternative ts more likely
than another.
overlap considerably across conditions. Although the values in the second
phase are collectively lower than in the other two conditions, the decrease is
modest. This level of variability begs for not only a greater number of observa
tions under each condition but efforts to either reduce variability or at least
strengthen the effects of the intervention. Reducing variability need not focus
only on controlling extraneous factors. Sometimes the best solution is to
reconsider the general conditions running throughout control and experi
mental phases, not to mention the features of the intervention condition itself.
The full array of measurement decisions can also be reviewed.
not obtained under most conditions. Obtaining the needed steady states might
well have resolved the problem presented by each case. Having a clear picture
of stable responding under each condition will not decide all interpretive
issues, but it is at least a necessary component of any solution. Order an Essay Now & Get These Features For Free:
