efa_screen.md
In EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

print output is stable

Code
  print(scr_cor)
Output

  -- Sampling adequacy and sphericity --------------------------------------------

  v The overall KMO value for your data is marvellous (Overall KMO = <num>).
  These data are probably suitable for factor analysis (verbal bands: Kaiser &
  Rice, 1974).

  v The Bartlett's test of sphericity was significant at an alpha level of <num>.
  These data are probably suitable for factor analysis.
  χ²(153) = <num>, p < <num>

  -- Multicollinearity -----------------------------------------------------------

  i Determinant: <num>. It falls as variables are added, so the condition index
  below carries the verdict.
  v Condition number: <num> (condition index <num>). An index of 10 or less is
  rarely of interest (Belsley, 1991).

  -- Per-variable diagnostics ----------------------------------------------------

        MSA   SMC
  V1 <num> <num>
  V2 <num> <num>
  V3 <num> <num>
  V4 <num> <num>
  V5 <num> <num>
  V6 <num> <num>
  V7 <num> <num>
  V8 <num> <num>
  V9 <num> <num>
  V10 <num> <num>
  V11 <num> <num>
  V12 <num> <num>
  V13 <num> <num>
  V14 <num> <num>
  V15 <num> <num>
  V16 <num> <num>
  V17 <num> <num>
  V18 <num> <num>

  -- Recommendations -------------------------------------------------------------

  v The data appear suitable for factor analysis.
  i Per-item variance, missing-data, category, normality, and outlier diagnostics
    require raw data; only a correlation matrix was supplied.

Code
  print(scr_raw)
Output

  -- Sampling adequacy and sphericity --------------------------------------------

  v The overall KMO value for your data is marvellous (Overall KMO = <num>).
  These data are probably suitable for factor analysis (verbal bands: Kaiser &
  Rice, 1974).

  v The Bartlett's test of sphericity was significant at an alpha level of <num>.
  These data are probably suitable for factor analysis.
  χ²(28) = <num>, p < <num>

  -- Multicollinearity -----------------------------------------------------------

  i Determinant: <num>. It falls as variables are added, so the condition index
  below carries the verdict.
  v Condition number: <num> (condition index <num>). An index of 10 or less is
  rarely of interest (Belsley, 1991).

  -- Per-variable diagnostics ----------------------------------------------------

            variance missing%   SMC   MSA  flags
  fun <num>        0 <num> <num>       
  friends <num>        0 <num> <num>       
  enjoy <num>        0 <num> <num> sparse
  hurt <num>        0 <num> <num> sparse
  part <num>        0 <num> <num>       
  commonly <num>        0 <num> <num>       
  chances <num>        0 <num> <num>       
  attracted <num>        0 <num> <num> sparse

  -- Multivariate normality ------------------------------------------------------

  x Mardia's skewness: χ²(120) = <num>, p < <num>.
  x Mardia's kurtosis: z = <num>, p < <num>.
  x Henze-Zirkler: HZ = <num>, p < <num>.
  These data depart from multivariate normality: 3 of the 3 tests reject it.

  -- Outliers --------------------------------------------------------------------

  ! A robust (MCD) covariance could not be computed; classical Mahalanobis
  distances were used.
  At least half the complete cases lie exactly on a lower-dimensional hyperplane
  (an "exact fit"). This is common with coarse discrete items, where many
  respondents give identical answers on an item pair; it does not mean the data
  are collinear at the correlation level.
  These distances come from a covariance the outliers themselves inflate, so the
  diagnostic is no longer high-breakdown and tends to under-flag.
  i 71 of 810 observations were flagged as multivariate outliers (Mahalanobis
  distance > <num>).

  -- Recommendations -------------------------------------------------------------

  ! These data depart from multivariate normality; normal-theory standard errors
    and fit statistics may be biased - prefer robust (sandwich) or bootstrapped
    standard errors.
  ! Bartlett's test is significant, but it assumes multivariate normality and
    grows more sensitive as N increases; because these data are non-normal, treat
    it as uninformative here and rely on the KMO.
  ! 3 variables have a sparse response category (< 5 responses): enjoy, hurt, and
    attracted; a low-frequency category can destabilise polychoric estimates -
    consider collapsing it into an adjacent category.
  ! 71 observations were flagged as potential multivariate outliers; inspect them
    (see `$outliers$flagged`) before down-weighting or excluding.

Code
  print(scr_non)
Output

  -- Sampling adequacy and sphericity --------------------------------------------

  v The overall KMO value for your data is marvellous (Overall KMO = <num>).
  These data are probably suitable for factor analysis (verbal bands: Kaiser &
  Rice, 1974).

  ! Bartlett's test of sphericity was not computed; no sample size (N) was
  supplied.

  -- Multicollinearity -----------------------------------------------------------

  i Determinant: <num>. It falls as variables are added, so the condition index
  below carries the verdict.
  v Condition number: <num> (condition index <num>). An index of 10 or less is
  rarely of interest (Belsley, 1991).

  -- Per-variable diagnostics ----------------------------------------------------

        MSA   SMC
  V1 <num> <num>
  V2 <num> <num>
  V3 <num> <num>
  V4 <num> <num>
  V5 <num> <num>
  V6 <num> <num>
  V7 <num> <num>
  V8 <num> <num>
  V9 <num> <num>
  V10 <num> <num>
  V11 <num> <num>
  V12 <num> <num>
  V13 <num> <num>
  V14 <num> <num>
  V15 <num> <num>
  V16 <num> <num>
  V17 <num> <num>
  V18 <num> <num>

  -- Recommendations -------------------------------------------------------------

  v The data appear suitable for factor analysis.
  i Per-item variance, missing-data, category, normality, and outlier diagnostics
    require raw data; only a correlation matrix was supplied.

the report tracks a narrow console

Code
  print(scr_cor)
Output

  -- Sampling adequacy and sphericity ------------------------

  v The overall KMO value for your data is marvellous
  (Overall KMO = <num>).
  These data are probably suitable for factor analysis
  (verbal bands: Kaiser & Rice, 1974).

  v The Bartlett's test of sphericity was significant at an
  alpha level of <num>.
  These data are probably suitable for factor analysis.
  χ²(153) = <num>, p < <num>

  -- Multicollinearity ---------------------------------------

  i Determinant: <num>. It falls as variables are added, so
  the condition index below carries the verdict.
  v Condition number: <num> (condition index <num>). An
  index of 10 or less is rarely of interest (Belsley, 1991).

  -- Per-variable diagnostics --------------------------------

        MSA   SMC
  V1 <num> <num>
  V2 <num> <num>
  V3 <num> <num>
  V4 <num> <num>
  V5 <num> <num>
  V6 <num> <num>
  V7 <num> <num>
  V8 <num> <num>
  V9 <num> <num>
  V10 <num> <num>
  V11 <num> <num>
  V12 <num> <num>
  V13 <num> <num>
  V14 <num> <num>
  V15 <num> <num>
  V16 <num> <num>
  V17 <num> <num>
  V18 <num> <num>

  -- Recommendations -----------------------------------------

  v The data appear suitable for factor analysis.
  i Per-item variance, missing-data, category, normality, and
    outlier diagnostics require raw data; only a correlation
    matrix was supplied.