---
title: "SEM-Based SSM Analysis"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{SEM-Based SSM Analysis}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
``` r
library(circumplex)
```
The other vignettes model *observed* circumplex scores: the mean profile of a
group, or the profile of correlations between the circumplex scales and an
external measure. This vignette introduces a **latent** counterpart, built on
a structural equation model (SEM) of the circumplex scales, and exposed
through `ssm_sem()` and its helpers. It teaches two products — the latent
profile of a measure and the invariance-gated latent contrast between groups —
how their confidence intervals are constructed, and the assumptions that make
them interpretable.
The latent-level Structural Summary Method appears to be new: a search of the
SEM–circumplex literature (2019 onward) found related work on the latent
structure of circumplex *scales* (e.g., Wendt et al., 2019) and on inference
for disattenuated correlations (Moss, 2026), but no prior work applying the
SSM decomposition — elevation, amplitude, displacement, and fit — to a
measure's *latent* profile with circular-aware intervals. Treat this layer as
a research tool whose assumptions you should understand before relying on it.
## 1. Why a latent SSM?
An observed correlation profile is *attenuated* by measurement error: because
each circumplex scale is imperfectly reliable, the correlations between the
scales and an external measure are pulled toward zero, and — because the
scales are not equally reliable — pulled toward zero by *different amounts*.
The observed SSM parameters inherit both effects. Amplitude is deflated by the
average unreliability, and displacement is rotated by the *heterogeneity* of
reliability around the circle.
The latent SSM estimates the profile the measure would show against the
**latent circumplex content** of the scales — the disattenuated analog of the
observed correlation profile (Zimmermann & Wright, 2017, defined the observed
version; this is its latent counterpart). It fits a measurement model in which
each scale loads on latent factors placed at that scale's *fixed theoretical
angle*, then reads the measure's correlations with the common circumplex
content and summarizes them with the usual SSM transform.
Two consequences are worth stating up front, because they shape everything
below:
- The latent profile is **model-conditional**. Every latent parameter is
conditional on the fixed-angle measurement model being an adequate
description of the data. A poorly fitting model does not make the latent
parameters merely imprecise; it makes them uninterpretable. Global fit is
therefore reported alongside every result.
- The angles are **theoretical claims, not estimates**. `ssm_sem()` never
estimates an angle. If an instrument's real geometry departs from theory,
that departure is absorbed into misfit, not into the angles. To *examine*
circumplex geometry — to let the angles be free — use `cpm_fit()`, which
fits Browne's (1992) circumplex model; that is a different question and a
different tool.
## 2. The measurement model
`ssm_sem()` builds and fits a lavaan measurement model for you, but it is
worth seeing the model it generates. `ssm_sem_syntax()` returns that model as
a string:
``` r
scales <- c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO")
syntax <- ssm_sem_syntax(scales = scales, angles = octants(), measures = "NARPD")
cat(syntax)
#> # circumplex SSM measurement model (generated by ssm_sem_syntax())
#> # scales: PA, BC, DE, FG, HI, JK, LM, NO
#> # angles (degrees): 90, 135, 180, 225, 270, 315, 360, 45
#> # model tier: scaled
#>
#> # general factor: free per-scale saturations
#> g =~ NA*PA + a1*PA + a2*BC + a3*DE + a4*FG + a5*HI + a6*JK + a7*LM + a8*NO
#> # circumplex plane: loadings free but with each scale's angle fixed
#> cx =~ NA*PA + lx1*PA + lx2*BC + lx3*DE + lx4*FG + lx5*HI + lx6*JK + lx7*LM + lx8*NO
#> cy =~ NA*PA + ly1*PA + ly2*BC + ly3*DE + ly4*FG + ly5*HI + ly6*JK + ly7*LM + ly8*NO
#> # fixed-angle direction constraints: sin(a)*lx - cos(a)*ly == 0
#> 0 == 1*lx1 - 0*ly1
#> 0 == 0.70710678118654757*lx2 - -0.70710678118654746*ly2
#> 0 == 0*lx3 - -1*ly3
#> 0 == -0.70710678118654746*lx4 - -0.70710678118654768*ly4
#> 0 == -1*lx5 - 0*ly5
#> 0 == -0.70710678118654768*lx6 - 0.70710678118654735*ly6
#> 0 == 0*lx7 - 1*ly7
#> 0 == 0.70710678118654746*lx8 - 0.70710678118654757*ly8
#> # isotropic orthonormal plane metric (plane scale absorbed by loadings)
#> g ~~ 1*g
#> cx ~~ 1*cx
#> cy ~~ 1*cy
#> cx ~~ 0*cy
#> # general-plane covariances fixed to zero: with free per-scale
#> # saturations, freeing these is locally unidentified exactly at
#> # phi_g = 0 (the trade a_i +/- d*c_i*cos/sin(angle_i) <-> phi_g is
#> # first-order flat there), so they cannot be estimated. To model a
#> # general factor leaning into the plane, use the strict tier, whose
#> # fixed loadings leave the full factor covariance matrix free.
#> g ~~ 0*cx
#> g ~~ 0*cy
#>
#> # external measure(s): related to circumplex factors
#> NARPD ~~ mg1*g
#> NARPD ~~ mcx1*cx
#> NARPD ~~ mcy1*cy
#>
#> # NOTE: amplitude (a) and displacement (d) are deliberately NOT defined
#> # here. They are nonlinear (sqrt / atan2) and their intervals must be
#> # built in-package through circular quantiles, never via lavaan := or
#> # delta-method CIs (which ignore the angular branch cut).
```
Three latent factors carry the structure: a general factor `g` and the two
plane axes `cx` and `cy`. Each scale loads on all three, but its plane loadings
are tied to its **fixed angle** by a direction constraint
(`sin(θ)·lx − cos(θ)·ly == 0`), so the scale's location in the plane is
theoretical while its *saturation* (how strongly it expresses the circumplex)
is free. This is the default **`"scaled"`** tier. A stricter
**`"strict"`** tier instead fixes every loading to the unit cosine pattern and
frees the full factor covariance matrix; it is the fully theoretical benchmark,
and it is what remains identified when the number of scales is small.
The header and comments in the generated syntax record two design decisions
that matter statistically:
- Under the scaled tier the general factor is fixed **orthogonal** to the
plane (`g ~~ 0*cx`, `g ~~ 0*cy`). Freeing those covariances alongside free
per-scale saturations is locally unidentified exactly at the null they would
test, so they cannot be estimated in this tier. A general factor that leans
into the plane is expressible only under the strict tier (or surfaces as
misfit under the scaled tier). This matters for real interpersonal data:
Wendt et al. (2019) found a general–agency correlation of roughly −.3
replicated across four samples, so on IIP-family instruments the
orthogonality the scaled tier assumes is known to be violated.
- The final `NOTE` says amplitude and displacement are **deliberately not
defined** in the lavaan syntax. That is the subject of Section 4.
You rarely call `ssm_sem_syntax()` directly — `ssm_sem()` does — but it is the
escape hatch for respecifications (for example, partial invariance) that you
then fit yourself and hand back through `ssm_sem_parameters()`.
## 3. Estimating a latent profile
The everyday entry point is `ssm_sem()`. Its arguments mirror `ssm_analyze()`:
the data, the `scales` and their `angles`, and one or more `measures`. The
`measures` argument is required in the single-group case — the single-group
latent SSM *is* the correlation path (a single-group latent *mean* profile is
not identified, so it is not offered).
Because the confidence intervals are simulated, set a seed immediately before
the call for reproducibility.
``` r
data("jz2017")
set.seed(12345)
latent <- ssm_sem(
jz2017,
scales = scales,
angles = octants(),
measures = "NARPD",
boots = 500
)
latent
#>
#> # Latent (SEM-based) SSM
#>
#> Measurement model: scaled fixed-angle circumplex
#> Global fit (N = 1166, robust): chisq(17) = 300.546, p < 0.001
#> CFI = 0.93, RMSEA = 0.13, SRMR = 0.072
#>
#> # Profile [NARPD]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.249 0.210 0.295
#> X-Value -0.009 -0.054 0.033
#> Y-Value 0.231 0.189 0.273
#> Amplitude 0.232 0.192 0.274
#> Displacement 92.132 82.515 104.461
#> Model Fit 0.975
```
Compare this with the *observed* correlation profile of the same measure:
``` r
set.seed(12345)
observed <- ssm_analyze(
jz2017,
scales = scales,
angles = octants(),
measures = "NARPD"
)
observed
#>
#> # Profile [NARPD]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.202 0.169 0.238
#> X-Value -0.062 -0.094 -0.029
#> Y-Value 0.179 0.145 0.213
#> Amplitude 0.189 0.154 0.227
#> Displacement 108.967 98.633 118.537
#> Model Fit 0.957
```
The two profiles tell the same broad story — narcissistic PD relates to the
upper (dominant) region of the circumplex — but the disattenuated profile has
a **larger amplitude** and a **higher fit**, and its displacement sits at a
somewhat different angle. The amplitude increase is the removal of attenuation:
the latent correlations are not pulled toward zero by scale unreliability. The
displacement shift and the fit increase are the removal of *reliability
heterogeneity* around the circle (Section 5 explains why each moves).
`ssm_sem()` returns a `circumplex_ssm_sem` object, a subclass of the ordinary
`circumplex_ssm` object, so the familiar table and plot functions work on it:
``` r
ssm_plot_circle(latent)
```
``` r
knitr::kable(
ssm_table(latent, render = FALSE),
caption = "Latent SSM profile of NARPD"
)
```
Table: Latent SSM profile of NARPD
|Profile |Elevation |X.Value |Y.Value |Amplitude |Displacement |Fit |
|:-------|:-----------------|:-------------------|:-----------------|:-----------------|:------------------|:-----|
|NARPD |0.25 (0.21, 0.29) |-0.01 (-0.05, 0.03) |0.23 (0.19, 0.27) |0.23 (0.19, 0.27) |92.1 (82.5, 104.5) |0.975 |
By default `ssm_sem()` fits with a robust estimator (`estimator = "MLR"`) and
reports robust global fit indices, because circumplex scale scores are
typically skewed and the naive chi-square over-rejects. The robust
(sandwich) standard errors also feed the confidence intervals — for a reason
that is the subject of the next section.
## 4. Where the confidence intervals come from
Amplitude and displacement are **nonlinear** functions of the model
parameters: amplitude is a square root and displacement is an `atan2`. lavaan
can attach a delta-method or bootstrap-percentile interval to any derived
quantity, but for displacement those intervals are wrong in a way that is easy
to miss. `atan2` has a branch cut: a displacement near 0°/360° can produce an
interval that has been unwrapped across the cut, or whose endpoints have
sign-flipped, so a naive percentile interval straddling the boundary points
the wrong way around the circle.
`circumplex` therefore **never delegates amplitude or displacement intervals
to lavaan** — the reason the generated syntax refuses to define them. Instead
it reuses the same machinery the observed bootstrap uses:
1. lavaan supplies only the point estimates and their covariance (or bootstrap
replicates) of the model's *free* parameters.
2. `ssm_sem()` draws from that covariance, maps each draw through the profile
and the SSM transform, and obtains a replicate of every SSM parameter.
3. Those replicates go through the package's existing interval assembly —
percentile intervals for the linear parameters, and **circular quantiles**
for displacement (centered on the circular mean, unwrapped, quantiled, and
re-wrapped), with contrast intervals aligned to the estimate's branch.
The result is that a latent displacement interval behaves correctly at the
0°/360° pole: it can straddle the boundary contiguously, and the point estimate
always sits inside its own interval. This is the same architecture the Monte
Carlo engine uses for observed profiles, with lavaan supplying the mean and
covariance rather than resampling.
Two engines feed step 2, selected by `ci_method`:
- **`"mvn"` (default):** draw from a multivariate normal centered at the
estimates with the model's (robust) covariance. Fast — one model fit plus
vectorized draws.
- **`"boot"`:** refit the model on each bootstrap resample. Far more
expensive, but it does not lean on asymptotic normality.
A coverage study across constructed populations (reported in the package's
design notes) found `"mvn"` well-calibrated at realistic sample sizes *when the
covariance is the robust sandwich* — which is why robust SEs are the default.
Under a misspecified fixed-angle model, plain (non-robust) SEs undercovered
displacement; the sandwich restored nominal coverage. If you supply your own
lavaan fit through `ssm_sem_parameters()` and intend to use `"mvn"`, fit it
with `se = "robust.huber.white"` so the propagated covariance stays valid.
## 5. What the parameters mean now
Disattenuation changes what two of the parameters *mean*, and the vignette
would be misleading if it did not say so.
**Fit.** Under the scaled tier the latent profile is not forced to be a perfect
cosine — the scales have different saturations — so a latent fit below 1 is
*informative*: it measures how far the measure's latent profile departs from a
pure cosine wave, driven by differential saturation across scales (and, under
the strict tier, by anisotropy in the factor covariance). What the latent fit
removes, relative to the observed fit, is the contamination from
**reliability heterogeneity** — not sampling error. Both the observed and the
latent fit are population quantities that contain no sampling error at all; as
*estimates* at a finite sample size, both remain noisy, so a latent fit of,
say, .85 in a modest sample is not automatically substantive structure.
**Displacement.** Latent displacement is the first-harmonic direction of the
*saturation-modulated* disattenuated profile. It is **not** simply "the
measure's angle in the latent space." Heterogeneous saturations around the
circle, or a general factor leaning into the plane, rotate it — with fit
possibly staying high — exactly as they rotate the observed displacement. The
latent layer's contribution is the removal of the *reliability* modulation
that additionally rotates the observed displacement; it does not remove the
saturation modulation. That removal is why the observed and latent
displacements in Section 3 differ, and it is the honest description of what
`d` estimates here.
The interpretation aids you already know carry over unchanged. Amplitude is the
gate for interpreting displacement: when the amplitude confidence interval's
lower bound sits too close to zero relative to its width, the profile has no
well-defined direction and the displacement is not interpretable — the low-fit
dashing on plots and the displacement caution in `print()` behave exactly as
they do for observed profiles.
## 6. Two questions about group differences
When you have groups, there are **two** distinct estimands, and `circumplex`
keeps them separate on purpose.
Table: Two estimands for a group difference
|Question |Estimand |Tool |Confounds |
|:-------------------------------------------------------------------------------------------|:-----------------|:----------------------------|:--------------------------------------------------------------------------------------------|
|Do the groups' *measured* profiles differ? |Observed contrast |ssm_analyze(contrast = TRUE) |Structural difference, differential reliability, and non-invariance are combined. |
|Do the groups' *constructs* differ, granted the instrument measures the same thing in both? |Latent contrast |ssm_sem(contrast = TRUE) |Disattenuated and conditional on measurement invariance; not computed when invariance fails. |
Neither is more correct in the abstract. The observed contrast answers a
question about *scores* and is always available. The latent contrast answers a
question about *constructs*, but only *if* the instrument behaves the same way
in both groups — and when it does not, the honest answer is that the groups
cannot be compared on the latent metric, not a number.
## 7. Invariance-gated latent contrasts
Before it computes a latent group contrast, `ssm_sem()` fits an invariance
ladder — configural, then metric, then scalar — and tests each rung against
the previous one with lavaan's own nested-model test (the scaled difference
test under the robust estimator). The latent *measure-profile* contrast
requires **metric** invariance (equal saturations across groups); the latent
*mean* contrast additionally requires scalar invariance. If the required rung
is rejected, the contrast is **not** computed.
On real data this gate does its job. Comparing the NARPD profile across the
`Gender` groups in `jz2017` rejects metric invariance, so `ssm_sem()` returns
each group's separate profile and an explicit non-comparison verdict rather
than a contrast:
``` r
set.seed(12345)
by_gender <- ssm_sem(
jz2017,
scales = scales,
angles = octants(),
measures = "NARPD",
grouping = "Gender",
contrast = TRUE,
boots = 300
)
by_gender
#>
#> # Latent (SEM-based) SSM
#>
#> Measurement model: scaled fixed-angle circumplex
#> Global fit (N = 1166, robust): chisq(34) = 287.272, p < 0.001
#> CFI = 0.938, RMSEA = 0.123, SRMR = 0.06
#>
#> Invariance ladder (gate: metric, alpha = 0.05):
#> rung chisq df cfi rmsea dchisq ddf p dcfi
#> configural 287.272 34 0.938 0.123 NA NA NA
#> metric 337.356 48 0.927 0.112 54.781 14 < 0.001 -0.011
#> ΔCFI: Cheung & Rensvold (2002) criterion, alpha = .01, two-group ML
#> simulation scope. The cutoff is NOT validated for this configuration
#> (robust CFI), so the value is descriptive only, with no binary
#> verdict. Secondary and reported only -- the verdict below gates on
#> the nested chi-square difference test alone.
#> Verdict: metric invariance rejected (Δχ²(14) = 54.78, p < 0.0001, alpha = 0.05): these groups cannot be compared on this instrument's latent metric.
#> The requested latent contrast was therefore not computed; the rows below are each group's separate (configural) latent profile. The observed-score contrast (ssm_analyze()) answers its own, different question and remains available.
#>
#> # Profile [NARPD: Female]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.198 0.141 0.252
#> X-Value -0.023 -0.083 0.040
#> Y-Value 0.250 0.188 0.318
#> Amplitude 0.251 0.194 0.318
#> Displacement 95.206 81.443 110.519
#> Model Fit 0.966
#>
#>
#> # Profile [NARPD: Male]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.313 0.243 0.376
#> X-Value 0.012 -0.046 0.076
#> Y-Value 0.199 0.146 0.250
#> Amplitude 0.199 0.147 0.257
#> Displacement 86.611 70.075 103.660
#> Model Fit 0.977
```
The invariance ladder is printed with the decision, and no contrast is
rendered — `ssm_plot_contrast()` on this object would have nothing to draw.
This is deliberate: there is no `force = TRUE`. If you have a principled
partial-invariance model, fit it yourself and pass it to
`ssm_sem_parameters()`, which computes the contrast from whatever multi-group
fit you supply and leaves the comparability claim to you.
The ladder table also carries `dcfi`, the change in CFI from the previous
fitted rung. This is Cheung and Rensvold's (2002) secondary criterion, whose
general rule rejects an invariance step when CFI falls by more than .01. It is
**reported, never gating**: comparability, the verdict, and the model the
estimates are taken from are decided by the nested test alone. The two criteria
can disagree, and neither is a tiebreaker for the other — a change in CFI is
insensitive to sample size, which the nested test is not, so in a large sample
the nested test can reject a step whose CFI barely moves.
That direction is worth stating carefully, because the article gives it two
ways. Its Table 5 reports critical values that are the 1% *lower* tails of the
simulated null distributions, so a ΔCFI at or below one of them is the
1%-level evidence *against* invariance — the sense used here. The sentence
stating the general rule on the article's p. 251 reads the opposite way
relative to that same table. This package follows the simulation.
Read the retain/reject label narrowly on the occasions it appears. Cheung and
Rensvold simulated two groups, ML estimation, and multivariate normal data, and
examined Type I error only — not power — and robust CFI variants were not part
of their study. `ssm_sem()` therefore prints the label only for a two-group fit
estimated by ML whose CFI is the plain, non-robust one. Note that
`estimator = "ML"` is necessary but not sufficient: `missing = "fiml"` also
makes lavaan report a robust CFI, so a fit can be ML and still fall outside the
envelope. Because the default estimator is `"MLR"`, the ladder above prints its
`dcfi` value marked as outside that scope, with no verdict attached and the note
naming which condition applies. The same withholding covers a non-ML estimator
such as `"GLS"`, whose CFI is plain-named but whose fit function is not the one
the criterion was simulated under. That is a deliberate refusal rather than a
gap: extending the cutoff to a robust index, another estimator, or three or more
groups would take simulation work nobody has done.
When invariance is not the obstacle — for example, contrasting **two measures**
within one group, where no cross-group invariance is at stake — the contrast is
computed and behaves like the observed contrast (second measure minus first,
displacement differences via the circular branch machinery):
``` r
set.seed(12345)
contrast <- ssm_sem(
jz2017,
scales = scales,
angles = octants(),
measures = c("NARPD", "ASPD"),
contrast = TRUE,
boots = 500
)
contrast
#>
#> # Latent (SEM-based) SSM
#>
#> Measurement model: scaled fixed-angle circumplex
#> Global fit (N = 1166, robust): chisq(22) = 317.867, p < 0.001
#> CFI = 0.935, RMSEA = 0.114, SRMR = 0.068
#>
#> # Profile [NARPD]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.248 0.210 0.295
#> X-Value -0.009 -0.052 0.032
#> Y-Value 0.231 0.191 0.272
#> Amplitude 0.231 0.193 0.272
#> Displacement 92.119 82.715 103.513
#> Model Fit 0.974
#>
#>
#> # Profile [ASPD]:
#>
#> Estimate Lower CI Upper CI
#> Elevation 0.161 0.115 0.207
#> X-Value -0.043 -0.092 -0.002
#> Y-Value 0.250 0.208 0.295
#> Amplitude 0.254 0.216 0.298
#> Displacement 99.732 90.351 111.188
#> Model Fit 0.978
#>
#>
#> # Contrast [ASPD - NARPD]:
#>
#> Estimate Lower CI Upper CI
#> Δ Elevation -0.087 -0.138 -0.044
#> Δ X-Value -0.034 -0.084 0.019
#> Δ Y-Value 0.019 -0.028 0.066
#> Δ Amplitude 0.022 -0.025 0.066
#> Δ Displacement 7.613 -6.113 19.993
#> Δ Model Fit 0.003
```
``` r
ssm_plot_contrast(contrast)
```
The contrast block reports the difference in each SSM parameter with its
confidence interval. As with the observed contrast, an elevation or amplitude
difference whose interval excludes zero is a difference in that parameter; the
displacement difference is reported on the estimate's angular branch, so its
interval endpoints can legitimately fall outside ±180° near the boundary while
still containing the estimate.
## 8. When to trust it: limitations
The latent layer buys disattenuation at the price of a set of assumptions. The
documentation states them; the vignette should too.
- **Model-conditional.** Every latent quantity is conditional on the
fixed-angle model being adequate. Read the global fit first. As a real-data
benchmark, Wendt et al. (2019) reported RMSEA between .075 and .111 for the
fixed-loading circumplex CFA across four large samples — a real but imperfect
approximation (their model targets the octants' own latent structure, not an
external measure's profile, so the number is a benchmark, not a like-for-like
comparison). The example fits here are of the same order (RMSEA around .12)
and should likewise be read as approximations, not exact structure.
- **Fixed angles are theoretical.** Departures from the theoretical geometry
load into misfit, not into the angles. Use `cpm_fit()` to examine geometry.
- **Latent-plane stationarity is assumed, not tested.** The plane factors are
fixed isotropic and orthogonal; anisotropic latent dispersion surfaces only
as global misfit.
- **The scaled tier assumes the general factor is orthogonal to the plane.**
A true general-factor lean surfaces as misfit under the scaled tier; use the
strict tier to model it.
- **Displacement and fit have the disattenuated meanings of Section 5**, not
the naive "angle in latent space" and "cosine-ness" readings.
- **Disattenuated correlations can be large.** Removing attenuation moves
correlations toward ±1; values at or beyond 1 signal misspecification and are
refused rather than summarized.
- **Invariance gating is a modeling decision** with a default test, not an
oracle. The observed contrast remains available and answers its own
question. The secondary `dcfi` criterion printed beside it gates nothing,
and its .01 cutoff is validated only for two-group, ML, multivariate-normal
fits, for Type I error only — the package withholds the verdict everywhere
else rather than extrapolating it.
## 9. Relation to the literature
The nearest published models are the confirmatory factor analyses of the
interpersonal circumplex itself. Wendt et al. (2019) fit a three-factor
circumplex CFA with fixed unit-cosine plane loadings — the shape of this
package's strict tier — across four large samples, and found the fully
dimensional model competitive with categorical and hybrid alternatives. Their
estimand, though, is the latent structure of the *octant scales* and persons'
factor scores, not an external measure's disattenuated profile; their model is
context for the strict tier, not a validation target for the SSM estimand.
At the level of a single disattenuated correlation, Moss (2026) showed that
treating reliability as a *known* constant collapses interval coverage
(to roughly .35 in one scenario), whereas propagating reliability uncertainty
restores nominal coverage. That is exactly the logic behind fitting the model
and propagating its full covariance rather than plugging in reliability point
estimates. One estimand caveat: Moss's disattenuated correlation corrects
*both* variables for unreliability, whereas the latent SSM here corrects only
the *scale* side — the external measure remains an observed variable — so the
two are relatives, not the same quantity.
Finally, the two model families in this package meet at a single point: at a
general factor orthogonal to the plane, equal saturations, and equally spaced
angles, the fixed-loading circumplex CFA coincides with the one-harmonic,
equal-communality version of Browne's (1992) circumplex model that `cpm_fit()`
estimates. The SEM-based SSM sits on the fixed-angle side of that boundary; to
cross to freely estimated angles, use `cpm_fit()`.
## References
* Browne, M. W. (1992). Circumplex models for correlation matrices.
_Psychometrika, 57_(4), 469–497.
* Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes
for testing measurement invariance. _Structural Equation Modeling, 9_(2),
233–255.
* Moss, J. (2026). Inference for disattenuated correlations.
_Applied Psychological Measurement_. Advance online publication.
https://doi.org/10.1177/01466216261440511
* Wendt, L. P., Wright, A. G. C., Pilkonis, P. A., Nolte, T., Fonagy, P.,
Montague, P. R., Benecke, C., Krieger, T., & Zimmermann, J. (2019). The
latent structure of interpersonal problems: Validity of dimensional,
categorical, and hybrid models. _Journal of Abnormal Psychology, 128_(8),
823–839.
* Zimmermann, J., & Wright, A. G. C. (2017). Beyond description in
interpersonal construct validation: Methodological advances in the
circumplex Structural Summary Approach. _Assessment, 24_(1), 3–23.