Reliable Change Index (RCI)#
The rci
function calculates the Reliable Change Index (RCI) for single-case data. It provides a statistical assessment of whether the change between two phases (typically A and B) is statistically reliable, considering measurement error and reliability.
Function Overview#
The rci
function computes the RCI for a single-case dataset. It prints descriptive statistics, confidence intervals, and RCI values for the specified phases.
Arguments#
data
: DataFrame containing SCED data (single case only).dvar
: Name of the dependent variable column (default: “values”).pvar
: Name of the phase variable column (default: “phase”).rel
: Reliability of the measurement (default: 0.8).ci
: Confidence level (default: 0.95).graph
: Whether to display a graph of confidence intervals (default: False).phases
: Tuple of phases to include (default: (“A”, “B”)).
The function prints all results directly and does not return a value.
import scia as sc
📖 scia 1.101.0.dev6 - For Documentation, visit: https://ahsankhodami.github.io/scia/intro.html
Example 1: Single-Case RCI Calculation#
# Create a single-case AB design
df = sc.create_scd(
values=[5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19],
phase_starts={"A": 1, "B": 6}
)
# Compute and print RCI
sc.rci(df)
Reliable Change Index
Mean Difference = 9.267
Standardized Difference = 1.768
Standard error of differences = 0.849
Reliability of measurements = 0.8
Descriptives:
n mean SD SE
A-Phase 5 6.400000 1.341641 0.600000
B-Phase 6 15.666667 2.581989 1.154701
95% Confidence Intervals:
Lower Upper
A-Phase 5.217270 7.582730
B-Phase 13.390501 17.942832
Reliable Change Indices:
RCI
Jacobson et al. 15.444444
Christensen and Mendoza 10.920871
Example 2: RCI for Each Case in a Multiple-Case Design#
The rci
function is designed for single-case data. To use it with multiple cases, simply call it for each case in your list.
# Create multiple cases
charlotte_test = sc.create_scd(
values={"A": [5, 7, 10, 5, 12], "B": [7, 10, 18, 15, 14, 19]},
name="Charlotte",
mt=list(range(11))
)
theresa_test = sc.create_scd(
values={"A": [3, 4, 3, 5], "B": [7, 4, 7, 9, 8, 10, 12]},
name="Theresa",
mt=list(range(11))
)
antonia_test = sc.create_scd(
values={"A": [9, 8, 8, 7, 5, 7], "B": [6, 14, 15, 12, 16]},
name="Antonia",
mt=list(range(11))
)
mbd_test = [charlotte_test, theresa_test, antonia_test]
# Compute and print RCI for each case
for case in mbd_test:
print(f"\nCase: {case['case'].iloc[0]}")
sc.rci(case)
Case: Charlotte
Reliable Change Index
Mean Difference = 6.033
Standardized Difference = 1.219
Standard error of differences = 1.970
Reliability of measurements = 0.8
Descriptives:
n mean SD SE
A-Phase 5 7.800000 3.114482 1.392839
B-Phase 6 13.833333 4.622409 2.067204
95% Confidence Intervals:
Lower Upper
A-Phase 5.067867 10.532133
B-Phase 9.778394 17.888273
Reliable Change Indices:
RCI
Jacobson et al. 4.331681
Christensen and Mendoza 3.062961
Case: Theresa
Reliable Change Index
Mean Difference = 4.393
Standardized Difference = 1.458
Standard error of differences = 0.606
Reliability of measurements = 0.8
Descriptives:
n mean SD SE
A-Phase 4 3.750000 0.957427 0.428174
B-Phase 7 8.142857 2.544836 1.138085
95% Confidence Intervals:
Lower Upper
A-Phase 2.909551 4.590449
B-Phase 5.908949 10.376765
Reliable Change Indices:
RCI
Jacobson et al. 10.259504
Christensen and Mendoza 7.254565
Case: Antonia
Reliable Change Index
Mean Difference = 5.267
Standardized Difference = 1.368
Standard error of differences = 0.864
Reliability of measurements = 0.8
Descriptives:
n mean SD SE
A-Phase 6 7.333333 1.366260 0.611010
B-Phase 5 12.600000 3.974921 1.777639
95% Confidence Intervals:
Lower Upper
A-Phase 6.087089 8.579577
B-Phase 8.974247 16.225753
Reliable Change Indices:
RCI
Jacobson et al. 8.619607
Christensen and Mendoza 6.094982