Formats the evidence in a nomologR result as a table ready for a thesis,
dissertation, or manuscript, following APA 7 conventions: a bold table
number, an italic title, no vertical rules, and notes below the table in the
order general, specific, probability.
Arguments
- x
A result object:
nomo_cfa,nomo_reliability,nomo_validity,nomo_invariance, ornomo_network.- type
Which table to build. For
nomo_cfa:"loadings","fit", or"factor_correlations". Fornomo_validity:"discriminant"or"convergent". Fornomo_network:"hypotheses"or"fit". Other objects have one table each.- number
Optional table number, printed in bold as "Table 1".
- title
Optional title; a descriptive default is supplied.
- ...
Unused.
Value
A nomo_apa_table object, which prints in the console and renders
as a formatted table, with its title and notes, when knitted.
Details
Leading zeros follow the statistic, not the value. APA 7 drops the
leading zero only for statistics that cannot exceed 1. So p values,
correlations, reliability coefficients, and CFI are written .95, while TLI,
RMSEA, SRMR, and standardized loadings keep it, 0.95, because each can
exceed 1 in principle: TLI is not bounded above, and a standardized loading
does in an improper (Heywood) solution. Many published tables print
standardized loadings without the zero; this follows the rule as written.
No verdicts. Table notes keep the package's reference-value language. No cell reads PASS or FAIL, and fit indices are not labeled good or poor.
Discriminant evidence without the Fornell-Larcker matrix. For a
nomo_validity result, the "discriminant" table gives each pair of
constructs one row: the latent correlation with its 95% confidence interval
(Rönkkö & Cho, 2022), and the heterotrait-monotrait ratios HTMT2 (Roemer et
al., 2021) and HTMT (Henseler et al., 2015) when they were computed. It does
not print the correlation matrix with the square root of AVE on its
diagonal, because that comparison often misses discriminant-validity
problems (Henseler et al., 2015). AVE is convergent evidence and has its own
"convergent" table.
The rules come from the Publication Manual of the American Psychological Association (7th ed.), checked against Purdue OWL's APA 7 guides. Journal-specific templates are out of scope.
Experimental layout. The layout of these tables is experimental until
nomologR 1.0.0 and may be adjusted as APA style is applied to more tables.
Any change will be described in NEWS; see ?nomologR for the stability
policy.
References
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). doi:10.1037/0000165-000
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi:10.2307/3151312
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. doi:10.1007/s11747-014-0403-8
Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–An improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637-2650. doi:10.1108/IMDS-02-2021-0082
Rönkkö, M., & Cho, E. (2022). An updated guideline for assessing discriminant validity. Organizational Research Methods, 25(1). doi:10.1177/1094428120968614
Examples
model <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
'
cfa <- nomo_cfa(model, data = lavaan::HolzingerSwineford1939)
nomo_apa_table(cfa, "loadings", number = 1)
#> Table 1
#> Standardized Factor Loadings
#> ----------------------------
#> Item visual textual speed
#> ----------------------------
#> x1 0.77
#> x2 0.42
#> x3 0.58
#> x4 0.85
#> x5 0.86
#> x6 0.84
#> x7 0.57
#> x8 0.72
#> x9 0.67
#> ----------------------------
#> Note. Standardized loadings from a confirmatory factor analysis. Estimated
#> with ML; N = 301. Blank cells are loadings fixed to zero by the model.
nomo_apa_table(cfa, "fit", number = 2)
#> Table 2
#> Model Fit
#> ------------------------------------------------------------------------------
#> Model χ² df p CFI TLI RMSEA [90% CI] SRMR
#> ------------------------------------------------------------------------------
#> Measurement model 85.31 24 < .001 .931 0.896 0.092 [0.071, 0.114] 0.065
#> ------------------------------------------------------------------------------
#> Note. Estimated with ML; N = 301. CFI = comparative fit index; TLI =
#> Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR =
#> standardized root mean square residual. Fit indices are reported as evidence,
#> not against fixed cutoffs.
nomo_apa_table(nomo_reliability(cfa), number = 3)
#> Table 3
#> Reliability Estimates
#> -------------------
#> Construct ω α
#> -------------------
#> visual .61 .63
#> textual .89 .88
#> speed .69 .69
#> -------------------
#> Note. ω = coefficient omega; α = coefficient alpha. Coefficient alpha assumes
#> equal loadings and is reported alongside omega for comparison with published
#> work.