SPCE 630 Final Exam prep Questions
and Answers Latest Versions 2025 A+
consistency
refers to the extent to which data patterns in one condition
are similar to data patterns in other conditions. confident
determination that a functional relation exists requires
consistency in data patterns between iterations of the
same condition and inconsistency in data patterns
between different, adjacent conditions.
potential demonstrations of effect
a functional relation can be identified when a) there is a
sufficient number of this (three opportunities to
demonstrate behavior change contingent on condition
change) and b) visual analysis suggests that consistent
changes in data occur for all potential demonstrations
the presence of a functional relation can be confirmed
when
a) there is a successful attempt to replicate effects of a
condition b) similar conditions generate similar levels and
trends within (intra participant replication) and across (inter
participant replication) participants in a study. a minimum
of three demonstrations of behavior change is required to
establish experimental control
magnitude
if a functional relation is present, this, or amount of
behavior change may be of interest. magnitude of effect is
assessed by comparing the amount and consistency of
change across conditions and cases within a study that is
directly attributed to the intervention.
,systematic process for conducting visual analysis
1. adequate number of data points within conditions to
establish data patterns.
2. clear patterns within conditions in level, trend, or
stability
3. behavior change between adjacent conditions in level,
trend, and/or variability
4. degree of overlap and immediacy of change in data
patterns across adjacent conditions
5. consistency of changes across conditions and cases
6. predicted patterns of change
7. magnitude of change across conditions and cases
visual analysis requires a plan
a) deciding how often data will be graphed
b) considering how data will be graphically displayed
c) determining which data characteristics will be the focus
of within and between condition analyses
d) identifying design related criteria that will impact visual
analysis
determining a schedule for graphing data, you should
a) ensure data are graphed regularly enough to inform
decision making with respect to implementing the design
as planned and b) identify relevant threats to internal
validity that can be detected visually
identifying design related criteria
a) minimum number of sessions per condition and b)
explicit criteria for changing conditions
summative analysis should focus on
a) within condition data patterns were stable b)
hypothesized between condition shifts in data patterns
,were detected and c) these shifts consistently co occurred
with each change in condition
split middle method
a tool that can be used to estimate trend within conditions
and compare trends between conditions.
these are most useful when within condition trends or
between condition changes in trend are of primary interest
and data show moderate or high variability within
conditions.
stability envelopes
can be used to estimate stability in level or trend within
conditions. the primary advantage of this is to ensure
consistency in experimental decisions related to data
stability.
percentage of non overlapping data
may be used to estimate level change between two
adjacent conditions. the higher the PND, the more
consistent and abrupt the level change between adjacent
conditions. a PND of 100% indicates no overlap in the
ranges of values between two adjacent conditions.
baseline logic
serves as the foundation for all single case design
research. all SCDS are mere extensions of the basic A-B
paradigms, wherein behavior is measured repeatedly
across two adjacent conditions: baseline (A) and
intervention (B).
single case experimental designs
some authors refer to designs with at least three
demonstrations of effect as this.
non experimental variations
A-B designs
, referred to s the simple time series design, represents the
most basic non experimental SCD. this design requires
that the dependent variable be measured repeatedly
under controlled baseline (A) and intervention (B)
conditions.
A-B-A designs
the target behavior is repeatedly measured under baseline
(A1) and intervention (B) conditions. after the dependent
variable has stabilized during intervention, you reintroduce
the baseline condition (A2) to the target behavior. this
design is susceptible to numerous threats of internal and
external validity.
A-B-A-B Withdrawal design
also referred to as reversal design, has been one of the
most frequently used SCDs in behavioral research. this
design permits a clear and convincing demonstration of
experimental control because it requires the repeated
introduction and withdrawal of an intervention.
experimental
causal attributions can be made and functional relations
can be demonstrated.
internal validity in ABAB
maturation threats may be likely if the baseline or
intervention conditions occur for an extended period of
time. control for this by using condition lengths that are
sufficient length to establish data patterns, intervening on
behaviors that are unlikely to slowly improve over time in
the absence of intervention and removing the intervention
in the second baseline condition.
internval validity in ABAB
and Answers Latest Versions 2025 A+
consistency
refers to the extent to which data patterns in one condition
are similar to data patterns in other conditions. confident
determination that a functional relation exists requires
consistency in data patterns between iterations of the
same condition and inconsistency in data patterns
between different, adjacent conditions.
potential demonstrations of effect
a functional relation can be identified when a) there is a
sufficient number of this (three opportunities to
demonstrate behavior change contingent on condition
change) and b) visual analysis suggests that consistent
changes in data occur for all potential demonstrations
the presence of a functional relation can be confirmed
when
a) there is a successful attempt to replicate effects of a
condition b) similar conditions generate similar levels and
trends within (intra participant replication) and across (inter
participant replication) participants in a study. a minimum
of three demonstrations of behavior change is required to
establish experimental control
magnitude
if a functional relation is present, this, or amount of
behavior change may be of interest. magnitude of effect is
assessed by comparing the amount and consistency of
change across conditions and cases within a study that is
directly attributed to the intervention.
,systematic process for conducting visual analysis
1. adequate number of data points within conditions to
establish data patterns.
2. clear patterns within conditions in level, trend, or
stability
3. behavior change between adjacent conditions in level,
trend, and/or variability
4. degree of overlap and immediacy of change in data
patterns across adjacent conditions
5. consistency of changes across conditions and cases
6. predicted patterns of change
7. magnitude of change across conditions and cases
visual analysis requires a plan
a) deciding how often data will be graphed
b) considering how data will be graphically displayed
c) determining which data characteristics will be the focus
of within and between condition analyses
d) identifying design related criteria that will impact visual
analysis
determining a schedule for graphing data, you should
a) ensure data are graphed regularly enough to inform
decision making with respect to implementing the design
as planned and b) identify relevant threats to internal
validity that can be detected visually
identifying design related criteria
a) minimum number of sessions per condition and b)
explicit criteria for changing conditions
summative analysis should focus on
a) within condition data patterns were stable b)
hypothesized between condition shifts in data patterns
,were detected and c) these shifts consistently co occurred
with each change in condition
split middle method
a tool that can be used to estimate trend within conditions
and compare trends between conditions.
these are most useful when within condition trends or
between condition changes in trend are of primary interest
and data show moderate or high variability within
conditions.
stability envelopes
can be used to estimate stability in level or trend within
conditions. the primary advantage of this is to ensure
consistency in experimental decisions related to data
stability.
percentage of non overlapping data
may be used to estimate level change between two
adjacent conditions. the higher the PND, the more
consistent and abrupt the level change between adjacent
conditions. a PND of 100% indicates no overlap in the
ranges of values between two adjacent conditions.
baseline logic
serves as the foundation for all single case design
research. all SCDS are mere extensions of the basic A-B
paradigms, wherein behavior is measured repeatedly
across two adjacent conditions: baseline (A) and
intervention (B).
single case experimental designs
some authors refer to designs with at least three
demonstrations of effect as this.
non experimental variations
A-B designs
, referred to s the simple time series design, represents the
most basic non experimental SCD. this design requires
that the dependent variable be measured repeatedly
under controlled baseline (A) and intervention (B)
conditions.
A-B-A designs
the target behavior is repeatedly measured under baseline
(A1) and intervention (B) conditions. after the dependent
variable has stabilized during intervention, you reintroduce
the baseline condition (A2) to the target behavior. this
design is susceptible to numerous threats of internal and
external validity.
A-B-A-B Withdrawal design
also referred to as reversal design, has been one of the
most frequently used SCDs in behavioral research. this
design permits a clear and convincing demonstration of
experimental control because it requires the repeated
introduction and withdrawal of an intervention.
experimental
causal attributions can be made and functional relations
can be demonstrated.
internal validity in ABAB
maturation threats may be likely if the baseline or
intervention conditions occur for an extended period of
time. control for this by using condition lengths that are
sufficient length to establish data patterns, intervening on
behaviors that are unlikely to slowly improve over time in
the absence of intervention and removing the intervention
in the second baseline condition.
internval validity in ABAB
