Descriptive Statistics in Single-Case Experimental Design

Descriptive Statistics in Single-Case Experimental Design#

The describe function calculates comprehensive descriptive statistics for single-case experimental design (SCED) datasets. It provides detailed statistical information for each phase and case in the data.

Computing Descriptive Statistics with describe#

The describe function computes various descriptive statistics for each phase in a single-case dataset. It handles multiple cases and phases, providing a comprehensive statistical overview.

Required Arguments:#

  • data: A Pandas DataFrame or list of DataFrames containing SCED data.

Optional Arguments:#

  • dvar: The column name of the dependent variable (default determined automatically).

  • pvar: The column name of the phase variable (default determined automatically).

  • mvar: The column name representing measurement time (default determined automatically).

  • return_dict: If True, returns a dictionary with additional metadata (default False).

The function calculates the following statistics for each phase:

  • n: Number of observations

  • mis: Number of missing values

  • m: Mean

  • md: Median

  • sd: Standard deviation

  • mad: Median absolute deviation

  • min: Minimum value

  • max: Maximum value

  • trend: Linear trend coefficient

import scia as sc

📖 scia 1.101.0.dev6 - For Documentation, visit: https://ahsankhodami.github.io/scia/intro.html

Example 1: Basic Descriptive Statistics#

This example demonstrates how to compute basic descriptive statistics for a simple AB design dataset.

import pandas as pd
import numpy as np

df = sc.create_scd(
    values=[5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19],
    phase_starts={"A": 1, "B": 6}
)

# Compute descriptive statistics
sc.describe(df)
Case Design n.A mis.A m.A md.A sd.A mad.A min.A max.A trend.A n.B mis.B m.B md.B sd.B mad.B min.B max.B trend.B
0 Case1 A-B 5.0 0.0 6.4 7.0 1.341641 1.4826 5.0 8.0 0.2 6.0 0.0 15.666667 15.5 2.581989 2.9652 12.0 19.0 0.742857 
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))  # Force mt to start from 0
)

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))  # Force mt to start from 0
)

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))  # Force mt to start from 0
)

mbd_test = [charlotte_test, theresa_test, antonia_test]
sc.describe(mbd_test)
Case Design n.A mis.A m.A md.A sd.A mad.A min.A max.A trend.A n.B mis.B m.B md.B sd.B mad.B min.B max.B trend.B
0 Case1 A-B 5.0 0.0 7.800000 7.0 3.114482 2.9652 5.0 12.0 1.200000 6.0 0.0 13.833333 14.5 4.622409 5.9304 7.0 19.0 1.971429
1 Case2 A-B 4.0 0.0 3.750000 3.5 0.957427 0.7413 3.0 5.0 0.500000 7.0 0.0 8.142857 8.0 2.544836 1.4826 4.0 12.0 1.000000
2 Case3 A-B 6.0 0.0 7.333333 7.5 1.366260 0.7413 5.0 9.0 -0.571429 5.0 0.0 12.600000 14.0 3.974921 2.9652 6.0 16.0 1.800000
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