SPCE 630 Final Exam prep 2023 with complete solutions
visual analysis of graphed data
is the cornerstone of and most frequently used data analysis method in SCD research,
particularly for determining whether a study demonstrates experimental control.
involves systematic procedures used to evaluate specific characteristics of data
patterns and evaluate the presence of a functional relation.
advantages of visual analysis
-can be used to evaluate data of individuals or small groups depending on the unit of
analysis specified in the research question.
-data are collected repeatedly, graphed as they are collected, and analyzed frequently
-focuses on analysis of individual data patterns
-discovery of potentially interesting findings that may not be directly related to the
original research question or program objective.
-graphic presentation of data permits independent analysis and interpretation of results
formative visual analysis
conducted within and across conditions to identify behavior change during the course of
a study.
behavior change
occurs when data patterns in one condition are different from data patterns in the
subsequent, adjacent condition for the same variable(s).
summative visual analysis
conducted following study completion, across multiple opportunities to demonstrate
behavior change to determine whether a functional relation exists between the
independent variable and the dependent variable.
adjacent conditions
in SCD research, data patterns are examined within and across adjacent conditions;
when data in one condition differ from what is predicted based on the preceding
condition, behavior change is demonstrated.
formative analysis is conducted in two steps
1. within and across adjacent condition analyses
2. systematic examination of specific data characteristics
within condition visual analyses
are conducted to discern patterns within a single condition during a study. within
condition visual analyses of level, trend, and variability/stability are critical for
determining when to change conditions, deciding whether adaptations need to be made,
and providing information related to answering research questions.
beginning with the initial condition, typically baseline, you should look for stability of data
across a minimum of at least three to five sessions prior to changing conditions.
condition change criteria
should be made a priori based on hypothesized data patterns. these criteria will guide
both formative and summative decisions about experimental control.
level
refers to the amount of behavior that occurs, as indicated by the ordinate scale value.
the characteristic of highest interest for behavior change, and is generally described as
low, moderate, or high.
, trend
the slope and direction of a data series or the direction data are moving over time. three
characteristics can be described: trend directions, trend magnitude, and trend stability.
trend direction
referred to as accelerating, decelerating, or zero celebrating. trend can further be
characterized by magnitude, and is often described as steep or gradual and paired with
direction. also describe whether the direction of a trend is improving (therapeutic) or
deteriorating (contra-therapeutic).
contra therapeutic trend
represents a common data pattern in SCD data that might occur within a condition and
particularly prior to the introduction of the independent variable. this refers to trends that
are in the opposite direction of the hypothesized direction of improvement and can
establish need for the intervention.
variability
fluctuation from one data point to the next and is the opposite of stability; in data with no
trend, this can be summarized as the range of data values within a condition or as the
percentage of data points falling within a given stability envelope
stability
is predictability and consistency of data values within a condition. perceptions of this
can be influenced by scales and ranges of y axes.
between condition visual analysis
the objective of this is to identify if behavior change has occurred. in SCD research a
particular condition (B) is introduced and re-introduced to one (eg. A-B-A-B) or more
than one (multiple baseline design) data series to evaluate whether there is a functional
relation between independent and dependent variables.
functional relations
are unequivocal demonstrations that an independent variable produced reliable and
consistent change in a dependent variable. the purpose of SCD research is to
determine if behavior change occurs when the intervention is introduced, and whether
the behavior change can vive reliably replicated.
analysis of data across adjacent conditions entails determining:
a) changes in data patterns (level, trend, variability) b) immediacy of change c) amount
of overlapping data across adjacent conditions d) consistency of data patterns across
similar conditions
immediacy of change
across adjacent conditions is the degree to which behavior change occurs as soon as
the intervention is introduced. when a large change in level occurs immediately after
introduction of a new condition, it is referred to as an abrupt change in level, which is
indicative of an immediately powerful or immediately effective intervention.
overlap
refers to values of data in one condition that are in the same range of values of data in
the subsequent, adjacent condition.
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
visual analysis of graphed data
is the cornerstone of and most frequently used data analysis method in SCD research,
particularly for determining whether a study demonstrates experimental control.
involves systematic procedures used to evaluate specific characteristics of data
patterns and evaluate the presence of a functional relation.
advantages of visual analysis
-can be used to evaluate data of individuals or small groups depending on the unit of
analysis specified in the research question.
-data are collected repeatedly, graphed as they are collected, and analyzed frequently
-focuses on analysis of individual data patterns
-discovery of potentially interesting findings that may not be directly related to the
original research question or program objective.
-graphic presentation of data permits independent analysis and interpretation of results
formative visual analysis
conducted within and across conditions to identify behavior change during the course of
a study.
behavior change
occurs when data patterns in one condition are different from data patterns in the
subsequent, adjacent condition for the same variable(s).
summative visual analysis
conducted following study completion, across multiple opportunities to demonstrate
behavior change to determine whether a functional relation exists between the
independent variable and the dependent variable.
adjacent conditions
in SCD research, data patterns are examined within and across adjacent conditions;
when data in one condition differ from what is predicted based on the preceding
condition, behavior change is demonstrated.
formative analysis is conducted in two steps
1. within and across adjacent condition analyses
2. systematic examination of specific data characteristics
within condition visual analyses
are conducted to discern patterns within a single condition during a study. within
condition visual analyses of level, trend, and variability/stability are critical for
determining when to change conditions, deciding whether adaptations need to be made,
and providing information related to answering research questions.
beginning with the initial condition, typically baseline, you should look for stability of data
across a minimum of at least three to five sessions prior to changing conditions.
condition change criteria
should be made a priori based on hypothesized data patterns. these criteria will guide
both formative and summative decisions about experimental control.
level
refers to the amount of behavior that occurs, as indicated by the ordinate scale value.
the characteristic of highest interest for behavior change, and is generally described as
low, moderate, or high.
, trend
the slope and direction of a data series or the direction data are moving over time. three
characteristics can be described: trend directions, trend magnitude, and trend stability.
trend direction
referred to as accelerating, decelerating, or zero celebrating. trend can further be
characterized by magnitude, and is often described as steep or gradual and paired with
direction. also describe whether the direction of a trend is improving (therapeutic) or
deteriorating (contra-therapeutic).
contra therapeutic trend
represents a common data pattern in SCD data that might occur within a condition and
particularly prior to the introduction of the independent variable. this refers to trends that
are in the opposite direction of the hypothesized direction of improvement and can
establish need for the intervention.
variability
fluctuation from one data point to the next and is the opposite of stability; in data with no
trend, this can be summarized as the range of data values within a condition or as the
percentage of data points falling within a given stability envelope
stability
is predictability and consistency of data values within a condition. perceptions of this
can be influenced by scales and ranges of y axes.
between condition visual analysis
the objective of this is to identify if behavior change has occurred. in SCD research a
particular condition (B) is introduced and re-introduced to one (eg. A-B-A-B) or more
than one (multiple baseline design) data series to evaluate whether there is a functional
relation between independent and dependent variables.
functional relations
are unequivocal demonstrations that an independent variable produced reliable and
consistent change in a dependent variable. the purpose of SCD research is to
determine if behavior change occurs when the intervention is introduced, and whether
the behavior change can vive reliably replicated.
analysis of data across adjacent conditions entails determining:
a) changes in data patterns (level, trend, variability) b) immediacy of change c) amount
of overlapping data across adjacent conditions d) consistency of data patterns across
similar conditions
immediacy of change
across adjacent conditions is the degree to which behavior change occurs as soon as
the intervention is introduced. when a large change in level occurs immediately after
introduction of a new condition, it is referred to as an abrupt change in level, which is
indicative of an immediately powerful or immediately effective intervention.
overlap
refers to values of data in one condition that are in the same range of values of data in
the subsequent, adjacent condition.
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
