--- title: "Evaluating Circumplex Structure" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Evaluating Circumplex Structure} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ``` r library(circumplex) ``` **Level:** Intermediate. Read "Intermediate SSM Analysis" first. ## 1. Overview This vignette asks whether an instrument fits a circumplex, the first of the two conditions that an SSM interpretation rests on. Section 2, "Two questions to ask before interpreting an SSM analysis", states both conditions. Section 3, "Does the instrument fit a circumplex?", fits the circular process model with `cpm_fit()`, reads its fit indices, and compares its constrained variants. The Wrap-up lists what the page covered and names the next pages, and the References list the sources cited. ## 2. Two questions to ask before interpreting an SSM analysis The Structural Summary Method (SSM) condenses a circumplex profile into a few interpretable parameters: elevation, amplitude, displacement, and fit. `vignette("introduction-to-ssm-analysis")` defines each of them. That condensation rests on two assumptions. Both are testable, but they are often left untested: 1. **Does the instrument actually have circumplex structure?** The SSM locates a profile on a circle, at positions given by the scales' theoretical angles. If the scales do not really form a circumplex, the summary is distorted before any profile is computed. The same holds if they sit at very different angles than the theory says. 2. **Are the confidence intervals trustworthy at your sample size and profile?** Zimmermann and Wright (2017) showed that the accuracy of bootstrap SSM intervals depends on the sample size and the instrument's correlation structure. For amplitude and displacement, it also depends on how differentiated the profile really is in the population. Intervals that are accurate for one construct in your table can be inaccurate for the construct printed directly below it. This page and the two after it show how to answer both questions. `cpm_fit()` and `fit_structure()` answer question 1, and `ssm_ci_accuracy()` answers question 2. The examples use the `jz2017` dataset. It holds the same sample of 1,166 undergraduates that Zimmermann and Wright (2017, Study 5) analyzed. It has octant scores on the IIP-SC, and personality disorder (PD) scale scores from the PDQ-4+. ## 3. Does the instrument fit a circumplex? ### Fitting the circular process model `cpm_fit()` estimates Browne's (1992) circular process model (CPM), the confirmatory model behind the CIRCUM and CircE programs. Each scale gets an estimated angle on the circle and a *communality index* $\zeta$. The communality index is the correlation between the scale and the common circumplex "factor". Its square, $\zeta^2$, is the scale's *communality*, the share of its variance that the circumplex explains. The model also yields the usual covariance-structure fit indices. `cpm_fit()` accepts raw data or a correlation matrix. On the raw-data path, the default confidence intervals are bootstrapped. We lower `boots` from its default of 2000 to keep this vignette quick to build. In practice, use the default. This chunk prints two warnings: the CPM Hessian is ill-conditioned, and some bootstrap resamples were excluded. Both are expected for these data. The page "CPM Fits at a Boundary" explains the first. The second means that the intervals rest only on the resamples that were kept. ``` r data("jz2017") set.seed(12345) cpm <- cpm_fit(jz2017, scales = PANO(), angles = octants(), boots = 500) #> Warning: CPM Hessian is ill-conditioned (condition number 1.83e+14): angles #> may be clustered or parameters weakly determined. #> Warning: 11 of 500 bootstrap resamples were excluded (0 with a degenerate or #> non-positive-definite correlation matrix, 11 failing the convergence #> acceptance criterion); the confidence intervals are based on the remaining #> 489 replicates and are conditional on estimability. summary(cpm) #> #> Circular Process Model (Browne, 1992) #> Model: quasi-circumplex #> Harmonics (m): 3 #> Sample size (N): 1166 #> Reference scale: PA #> CI method: bootstrap #> Confidence level: 0.95 #> #> # Estimated angles and communality indices #> #> Scale Theory Angle lci uci Zeta lci uci #> PA 90 90.000 90.000 90.000 0.767 0.684 0.860 #> BC 135 125.074 113.548 137.069 0.931 0.872 1.000 #> DE 180 170.353 156.943 185.174 0.780 0.733 0.826 #> FG 225 195.425 185.811 206.523 0.861 0.822 0.910 #> HI 270 250.721 244.660 258.758 0.956 0.935 0.977 #> JK 315 269.491 261.813 279.412 0.942 0.929 0.957 #> LM 360 294.230 286.626 303.019 0.806 0.761 0.850 #> NO 45 11.305 2.426 20.913 1.000 1.000 1.000 #> #> # Correlation-function weights #> #> k Beta Beta_lci Beta_uci #> 0 0.450 0.418 0.481 #> 1 0.440 0.411 0.466 #> 2 0.074 0.065 0.087 #> 3 0.036 0.015 0.057 #> #> # Fit indices #> #> χ²(10) = 81.169, p = <1e-04 #> RMSEA = 0.078 [0.063, 0.094] (90% CI) #> SRMR = 0.042 #> CFI = 0.984 TLI = 0.956 #> AIC = 117.169 BIC = 208.273 #> #> # Residuals #> #> Largest absolute residual: 0.134 (PA – HI) #> #> # Diagnostics #> #> Note: a communality index reached its upper boundary (ζ > 0.995, a #> Heywood-type solution). #> Note: 11 of 500 bootstrap resamples were excluded (0 degenerate, 11 #> non-convergent); the intervals are based on 489 replicates and are #> conditional on estimability. #> #> Note: boundary/weak-identification markers fired: Heywood communality; #> small correlation-function weight; ill-conditioned Hessian. #> What has been measured about these markers covers analytic intervals only, #> and not every marker was measured; they are not validated as predictors of #> the bootstrap intervals shown here (see the vignette section 'When a fit #> sits at a boundary'). ``` Two parts of this output matter most for evaluating structure: - **The estimated angles.** Compare them with the theoretical angles, but read the comparison carefully. One scale is held fixed to identify the configuration (PA here, at 90°). So every other scale's departure is measured from that anchor, and a different anchor would redistribute the departures. Two things are worth separating. The *ordering* around the circle is preserved: the estimated angles run through the octants in the same cyclic order that the instrument assigns them. The *spacing* is not preserved. The gaps between circularly adjacent estimates run from under 20° to nearly 80°, against a theoretical 45°. The largest departure from a theoretical position is about 66°. Departures from perfect structure are common in well-validated circumplex instruments (Gurtman & Pincus, 2000). The model comparison below quantifies what this pattern costs: it is why forcing equal spacing fits these data poorly. - **The communality indices.** Scales with low $\zeta$ are poorly described by the circle. A profile peak at such a scale's angle means less than the same peak at the angle of a well-explained scale. The estimated configuration is easiest to read on the circle itself. The plot draws each scale at its estimated angle, at a radius equal to its communality ($\zeta^2$). It adds a joint confidence region where one is estimable. ``` r plot(cpm) ``` plot of chunk cpm_plot ### Reading the fit indices The `summary()` output reports the standard indices, and the standard benchmarks apply: - RMSEA at or below about .08 suggests adequate approximate fit, and above about .10 suggests poor fit (Browne & Cudeck, 1993). - SRMR at or below about .08 suggests acceptable average residuals (Hu & Bentler, 1999). - CFI and TLI near .95 or above suggest good comparative fit (Hu & Bentler, 1999). Treat these as conventions from the broader covariance-structure literature, not circumplex-specific laws. Two caveats come from the benchmark sources themselves. First, Browne and Cudeck call their own RMSEA thresholds "based on subjective judgment." They caution that such a figure "cannot be regarded as infallible or correct" (Browne & Cudeck, 1993). So read the cutoffs as conventions, not decision rules. Second, these benchmarks are least dependable at small samples. Hu and Bentler (1999) found that the ML-based TLI and RMSEA tend to *overreject* true-population models when the sample is small. (CFI is not among the indices they flag.) Circumplex analyses are often run at modest sample sizes. The SSM accuracy thresholds in "Confidence Interval Accuracy" span roughly $n = 50$ to $200$. At small $n$, a TLI or RMSEA that falls short of its benchmark can reflect the index's small-sample behavior as much as the model's fit. Two further cautions are circumplex-specific, and both come from this package's own validation simulations: - **Boundary solutions are common at realistic sample sizes**, including in the fit above. The page "CPM Fits at a Boundary" says what that means and what to do about it. - **Do not lean hard on the chi-square p value.** In simulations at field-typical sample sizes with octant-like population structures, the test statistic did not follow its nominal chi-square reference distribution. This held even for correctly specified models. Read the chi-square descriptively (bigger is worse), and prefer RMSEA/SRMR/CFI for judging approximate fit. For the same reasons, `summary()` prints a caution for analytic (Wald) intervals below $N = 2000$. When a boundary marker (defined in "CPM Fits at a Boundary") is present, the analytic caution prints for every $N$ below $N = 50000$. In validation, analytic intervals mis-covered in exactly those regimes. Analytic intervals are the only option on the correlation-matrix input path. Prefer the bootstrap (the raw-data default) when you have raw data. ### Comparing model variants Constrained variants make the structural question sharp. Is the instrument consistent with *equally spaced* scales? Is it consistent with *equal* communalities? The correlation-matrix path is convenient for this comparison, because its point estimates and fit indices are deterministic. So no bootstrap is needed. The first of these refits (`quasi-circumplex`) prints the same ill-conditioned Hessian warning that "CPM Fits at a Boundary" explains. ``` r R <- cpm$matrices$R # the sample correlation matrix stored by cpm_fit() fit_quasi <- cpm_fit( cormat = R, scales = PANO(), angles = octants(), n = nrow(jz2017), model = "quasi-circumplex" ) #> Warning: CPM Hessian is ill-conditioned (condition number 1.83e+14): angles #> may be clustered or parameters weakly determined. fit_equal <- cpm_fit( cormat = R, scales = PANO(), angles = octants(), n = nrow(jz2017), model = "equal-communality" ) fit_circulant <- cpm_fit( cormat = R, scales = PANO(), angles = octants(), n = nrow(jz2017), model = "circulant" ) ``` The table below has one row for each of the three fits. It shows the model name from `$details$model`, and the degrees of freedom and four fit indices from `$fit`. The indices are rounded to three decimals. (The code that builds the table is omitted.) ``` #> model df rmsea srmr cfi tli #> 1 quasi-circumplex 10 0.078 0.042 0.984 0.956 #> 2 equal-communality 17 0.100 0.063 0.956 0.928 #> 3 circulant 24 0.185 0.130 0.790 0.755 ``` The pattern reproduces what Zimmermann and Wright (2017, p. 14) reported for these data with CircE. The fully constrained model (equal spacing *and* equal communality, the `"circulant"` variant) fits poorly. Relaxing the equal-spacing constraint improves fit to the edge of the conventional benchmarks: marginal by RMSEA, and acceptable by SRMR and CFI. They reported CFI = .824, TLI = .795 and RMSEA = .169 for the fully constrained model. For the model with the equal-spacing constraint relaxed, they reported CFI = .958, TLI = .931 and RMSEA = .098. Do not expect the default fit's indices to match theirs to the digit. CIRCUM/CircE fit a covariance version of the model with free scaling constants. By default, `cpm_fit()` fits the correlation structure directly, and the two versions differ slightly at finite sample sizes. Pass `scaling = "free"` to fit their covariance parameterization and reproduce published CIRCUM/CircE output exactly. For the model test, the scaling choice does not matter here. With correlation input, the two families' test statistics are calibration-indistinguishable. In paired simulation at sample sizes 250–50,000, they differed by well under 1% of the model degrees of freedom. The free statistic never exceeds the default's on the same input, beyond numerical tolerance. This is because the free family nests the default and is also started from its solution. The default is recommended for routine inference, because free scaling adds parameters whose analytic standard errors are often undefined at small-to-moderate samples. The *conclusion* is the same: ordered octants with unequal spacing, and adequate approximate fit once equal spacing is not forced. A poor CPM fit does not make SSM output uncomputable. It makes the summary less meaningful, because the profile is referred to scale positions that the data contradict. If the ordering itself fails (scales out of sequence around the circle), SSM parameters should not be interpreted. ## Wrap-up Evaluating circumplex structure has two layers. The first is whether the instrument behaves like a circumplex in your sample (`cpm_fit()`). The second is whether the inferential machinery of the SSM can be trusted at your sample size and profile (`ssm_ci_accuracy()`). Both are one function call, and both change what you should claim more often than users expect. In particular, amplitude and displacement intervals earn their trust only when the profile is genuinely differentiated relative to the precision that your sample size affords. Two pages follow this one. "CPM Fits at a Boundary" explains the boundary note that the fit above printed and what to do when a marker fires. "Confidence Interval Accuracy" asks whether the intervals can be trusted at your sample size and profile. ## References * Browne, M. W. (1992). Circumplex models for correlation matrices. _Psychometrika, 57_(4), 469–497. * Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), _Testing structural equation models_ (pp. 136–162). Newbury Park, CA: Sage. * Grassi, M., Luccio, R., & Di Blas, L. (2010). CircE: An R implementation of Browne's circular stochastic process model. _Behavior Research Methods, 42_(1), 55–73. * Gurtman, M. B., & Pincus, A. L. (2000). Interpersonal Adjective Scales: Confirmation of circumplex structure from multiple perspectives. _Personality and Social Psychology Bulletin, 26_(3), 374–384. * Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. _Structural Equation Modeling, 6_(1), 1–55. * 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.