Using Circumplex Instruments¶

%load_ext rich
import numpy as np
from great_tables import GT

from circumplex import (
    get_instrument,
    ipsatize,
    norm_standardize,
    score,
    show_instruments,
)

The rich extension is already loaded. To reload it, use:
  %reload_ext rich

1. Overview of Instrument-related Functions¶

2. Loading and Examining Instrument Objects¶

Previewing the available instruments¶

show_instruments()

          The circumplex package currently includes 3 instruments          
┏━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃   ┃ Abbreviation ┃ Name                                                 ┃
┡━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 1 │ CSIG         │ Circumplex Scales of Interpersonal Goals             │
│ 2 │ IIPSC        │ Inventory of Interpersonal Problems Short Circumplex │
│ 3 │ IPIPIPC      │ IPIP Interpersonal Circumplex                        │
└───┴──────────────┴──────────────────────────────────────────────────────┘

Loading a specific instrument¶

csig = get_instrument("csig")
csig

Instrument(
    'CSIG: Circumplex Scales of Interpersonal Goals',
    '32 items, 8 scales, 1 normative data sets',
    'Lock (2014)',
    '< https://doi.org/10.1177/0146167213514280 >'
)

csig.info()

CSIG: Circumplex Scales of Interpersonal Goals
32 items, 8 scales, 1 normative data sets
Lock (2014)
< https://doi.org/10.1177/0146167213514280 >


The CSIG contains 8 scales:
├── PA (90°): Be authoritative
├── BC (135°): Be tough
├── DE (180°): Be self-protective
├── FG (225°): Be wary
├── HI (270°): Be conflict-avoidant
├── JK (315°): Be cooperative
├── LM (360°): Be understanding
└── NO (45°): Be respected


The CSIG is rated using the following 5-point scale:
  0. It is not at all important that...
  1. It is somewhat important that...
  2. It is moderately important that...
  3. It is very important that...
  4. It is extremely important that...



The CSIG currently has 1 normative data set(s):

1. 665 MTurkers from US, Canada, and India about interactions between nations
   Lock (2014)
   https://doi.org/10.1177/0146167213514280

3. Instrument-related Tidying Functions¶

iipsc = get_instrument("iipsc")
iipsc.info()

IIP-SC: Inventory of Interpersonal Problems Short Circumplex
32 items, 8 scales, 2 normative data sets
Soldz, Budman, Demby, & Merry (1995)
< https://doi.org/10.1177/1073191195002001006 >


The IIP-SC contains 8 scales:
├── PA (90°): Domineering
├── BC (135°): Vindictive
├── DE (180°): Cold
├── FG (225°): Socially avoidant
├── HI (270°): Nonassertive
├── JK (315°): Exploitable
├── LM (360°): Overly nurturant
└── NO (45°): Intrusive


The IIP-SC is rated using the following 5-point scale:
  0. Not at all
  1. Somewhat
  2. Moderately
  3. Very
  4. Extremely



The IIP-SC currently has 2 normative data set(s):

1. 872 American college students
   Hopwood, Pincus, DeMoor, & Koonce (2011)
   https://doi.org/10.1080/00223890802388665
2. 106 American psychiatric outpatients
   Soldz, Budman, Demby, & Merry (1995)
   https://doi.org/10.1177/1073191195002001006

import pandas as pd

raw_iipsc = pd.read_csv(
    "/Users/mitch/Documents/GitHub/python-circumplex/src/circumplex/data/raw_iipsc.csv"
)
GT(raw_iipsc.head())

Ipsatizing item-level data¶

ips_iipsc = ipsatize(
    data=raw_iipsc,
    items=np.arange(0, 32),
    append=False,
)
GT(ips_iipsc.round(2))

round(raw_iipsc.mean(axis=1, skipna=True), 2)

0    1.00
1    0.94
2    1.41
3    2.29
4    0.62
5    0.28
6    0.50
7    0.74
8    0.97
9    0.22
dtype: float64

round(ips_iipsc.mean(axis=1, skipna=True), 2)

0    0.0
1    0.0
2    0.0
3   -0.0
4    0.0
5    0.0
6    0.0
7    0.0
8    0.0
9    0.0
dtype: float64

Scoring item-level data¶

iipsc.info_scales()

The IIP-SC contains 8 scales:
├── PA (90°): Domineering
├── BC (135°): Vindictive
├── DE (180°): Cold
├── FG (225°): Socially avoidant
├── HI (270°): Nonassertive
├── JK (315°): Exploitable
├── LM (360°): Overly nurturant
└── NO (45°): Intrusive

scale_scores = score(
    data=raw_iipsc, items=np.arange(0, 32), append=False, instrument="iipsc"
)
GT(scale_scores.round(2))

Standardizing scale-level data¶

iipsc.info_norms()

The IIP-SC currently has 2 normative data set(s):

1. 872 American college students
   Hopwood, Pincus, DeMoor, & Koonce (2011)
   https://doi.org/10.1080/00223890802388665
2. 106 American psychiatric outpatients
   Soldz, Budman, Demby, & Merry (1995)
   https://doi.org/10.1177/1073191195002001006

z_scales = iipsc.norm_standardize(
    data=scale_scores,
    scales=np.arange(0, 8),
    sample_id=1,
    append=False,
)
GT(z_scales.round(2))

norm_df = norm_standardize(
    data=scale_scores,
    instrument="iipsc",
    scales=np.arange(0, 8),
    sample_id=1,
    append=False,
)

GT(norm_df.round(2))