2.1. The LM test

In multivariate analysis of CSA or SEM, two commonly used test statistics are the LM test and the Wald test. The LM test only requires estimating the restricted model, while the Wald test requires a more comprehensive model. Notably, the statistical theory for the LM test is more complex than for the Wald test. This study focuses on estimating the restricted model under various constraints. The LM test is particularly useful for guiding model modifications to improve fit, as it identifies the effects of freeing initially fixed parameters (Lee and Bentler, Reference Lee and Bentler1980; Bentler, Reference Bentler1986; Satorra, Reference Satorra1989; Sörbom, Reference Sörbom1989; Yuan and Liu, Reference Yuan and Liu2021).

Standard LM tests rely on a single snapshot of the initial model, which may not accurately identify missing parameters in population data. Consequently, model misspecifications can occur, leading to poor generalization to new samples. This limitation is particularly evident with small sample sizes, as the effectiveness of model modification using the LM test becomes compromised and susceptible to random variations (MacCallum et al., Reference MacCallum, Roznowski and Nectowitz1992; Yuan and Liu, Reference Yuan and Liu2021).

A model is deemed acceptable when its parameters align with theories and show good statistical fit to the data (Chou and Huh, Reference Chou, Huh and Hoyle2012). If a model has $q$ free parameters and an additional nondependent variable, where $r \lt q$, the LM test can be used to identify which fixed parameters should be freed for better model fit. This is done by computing the LM test statistic ${T_{LM}}$. The LM test employs forward specification searching, where a constraint in the initial model is proposed to be freed based on how much it would enhance model fit.

A model consists of both free and fixed parameters, with the latter included to specify the model. Let $\boldsymbol{\widehat {\theta\,}} $ be a vector of constrained estimators of $\boldsymbol{\theta} $ that satisfies the $r \lt q$ constraints $h\left( \boldsymbol{\theta} \right)$ = 0 when minimizing the fit function $F(\boldsymbol{\theta})$ for a given model. This is equivalent to minimizing the function of a constrained model while assuming $h( \boldsymbol{\theta}) = {\theta _r} = 0$. With $r$ constraints, there exists an $r \times 1$ constraint vector $h{( \boldsymbol{\theta})^{\prime}} = ({h_1}, \ldots ,{\text{ }}{h_r})$. When minimizing the fit function $F( \boldsymbol{\theta})$ with constraints of $h( \boldsymbol{\theta})$ = 0, matrices of derivatives $\boldsymbol{g} = \left( \frac{{\partial F}}{{\partial \boldsymbol{\theta} }} \right)$ and $\boldsymbol{L^{\prime}} = \left( {\frac{{\partial h}}{{\partial \boldsymbol{\theta} }}} \right)$ exist for the “forward search,” and there will be a vector of LMs, $\boldsymbol{\lambda}$, such that

(1)\begin{equation}\boldsymbol{\hat {g}} + {\text{ }}\boldsymbol{\hat {L}^{\prime}}\boldsymbol{\hat {\lambda}} = 0 \ \text{and} \ h\left( \boldsymbol{{\widehat {\theta\,}} } \right) = 0.\end{equation}

For the LM test to be applicable, several technical regularity conditions must be met, including the continuity of $\partial h/\partial {\boldsymbol{\theta}} $, model identification, positive definiteness of ${\boldsymbol{\Sigma }}$, linearly independent constraints, and full rank matrices of derivatives $\boldsymbol{g}$ and $\boldsymbol{{L}^{\prime}}$. In the context of constraints, the asymptotic covariance matrix can be derived from an information matrix H, augmented by the matrix of derivatives $\boldsymbol{{L}}$ and a null matrix O. Thus, the sample variance covariance matrix of the estimated parameter, $\sqrt n \left( \boldsymbol{\widehat {{\theta\,}} - {{\theta}} } \right)$, is given by the inverse of the Fisher information matrix of $\boldsymbol{{H}}( {\boldsymbol{\theta}})\!,$ associated with $q$ free parameters in ${\boldsymbol{\theta}} $ in the case of maximum likelihood (ML) estimation, and $\boldsymbol{{R}}$ gives the covariance matrix of the LMs $\sqrt n \left( \boldsymbol{\hat {{\lambda}} - {{\lambda}} } \right)$, which is derived from the inverse of the information matrix L. Therefore, we can define

(2)\begin{equation}{\left[ {\begin{array}{*{20}{c}} \boldsymbol{H}& \boldsymbol{L'} \\ \boldsymbol{L}& \boldsymbol{O} \end{array}} \right]^{ - 1}} = {\text{ }}\left[ {\begin{array}{*{20}{c}} \boldsymbol{{H^{ - 1}} - {\text{ }}{H^{ - 1}}L'{{\left( {L{H^{ - 1}}L'} \right)}^{ - 1}}}& \boldsymbol{{H^{ - 1}}L'{{\left( {L{H^{ - 1}}L'} \right)}^{ - 1}}} \\ \boldsymbol{{{\left( {L{H^{ - 1}}L'} \right)}^{ - 1}}L{H^{ - 1}}}& \boldsymbol{ - {{\left( {L{H^{ - 1}}L'} \right)}^{ - 1}}} \end{array}} \right] = {\text{ }}\left[ {\begin{array}{*{20}{c}} \boldsymbol{M}& \boldsymbol{T'} \\ \boldsymbol{T}& - \boldsymbol{{ R}} \end{array}} \right].\end{equation}

Under regularity conditions and the null hypothesis $\boldsymbol{{H}}( {\boldsymbol{\theta}})$, the LM test is available in two versions, and the multivariate LM statistics are asymptotically distributed as a ${\chi ^2}$ variate with $r$ degrees of freedom. They compute all constraints simultaneously:

(3)\begin{equation}{T_{LM}} = n{\boldsymbol{\hat {{\lambda}} ^{\prime}}}\boldsymbol{\widehat {{R\,}}^{ - 1}}\boldsymbol{\hat {{\lambda}}} \sim \chi _r^2. \end{equation}

A univariate LM statistic is used to test a single constraint and is distributed as a ${\chi ^2}$ variate with 1 $df$. This test is particularly useful for evaluating whether a specific parameter in the ${{\boldsymbol{\theta}} _r}$ vector is equal to 0:

(4)\begin{equation}{T_{LMi}} = n\boldsymbol{\hat {\lambda}} _i^2\boldsymbol{\widehat {{R\,}}^{ - 1}}\boldsymbol{\hat {{\lambda}} _i}\sim \chi _{r1}^2.\end{equation}

This is also known as a modification index in the LISREL program (Jöreskog and Sörbom, Reference Jöreskog and Sörbom1988). For Equations (3) and (4), $r$ is the number of nondependent constraints, and the matrices $\boldsymbol{{H}}$ and ${\boldsymbol{{L}}^{\prime}}$ have dimensions $q \times q$ and $q \times r$, respectively:

(5)\begin{equation}\boldsymbol{\hat \lambda} = {\left( {\boldsymbol{{\widehat L}}\boldsymbol{{\widehat H}}^{ - 1}}\boldsymbol{{{\widehat L}^{\prime}}} \right)}^{ - 1}\boldsymbol{\widehat L}\boldsymbol{\widehat H^{ - 1}}\boldsymbol{\widehat g\,}.\end{equation}

The LM test performance depends on factors like sample size, degrees of freedom, and the number of variables and parameters. Degrees of freedom are influenced by the number of parameters and variables. Higher $p$ and lower $q$ result in more degrees of freedom. The LM test considers all possible paths based on the degrees of freedom. With more degrees of freedom, the number of possible paths increases, increasing the risk of falsely identifying nonexistent paths in the true model, especially when there are few missing paths. However, this risk decreases with larger sample sizes, reducing reliance on chance occurrences.

2.2. The Wald test

The Wald test follows a backward stepwise procedure to identify which free parameter, starting with the one with the smallest Wald test statistic, should be removed from the model. This is done by including candidate parameters and performing univariate Wald tests on each. Parameters with statistically significant Wald test values are retained, and the process continues until no further parameters can be added. The Wald test statistic is calculated as follows:

(6)\begin{equation}W = n\boldsymbol{\widehat {\theta\,} _{\! r}^{\prime}}\left( {\boldsymbol{\widehat L}\boldsymbol{{\widehat H}}^{ - 1}\boldsymbol{{\widehat L}^{\prime}}} \right)\boldsymbol{\widehat {\theta\,} _{\!\! r}} \sim \chi _r^2,\end{equation}

where $\boldsymbol{\hat L{\text{ }}}$ is a quadratic form. The closer $\boldsymbol{\hat L{\text{ }}}$ is to 0, the more likely it is that the null hypothesis equals $0$ will be rejected. The univariate Wald statistics ${\hat \theta _i}{\text{ }}$ for each of the parameters in $\boldsymbol{\widehat {\theta\,} _{\!\! r}}$ can be expressed as

(7)\begin{equation}{W_i} = n{\hat \theta _i}\widehat H_{ii}^{ - 1}, {\hat \theta _i} = n\theta _i^2/{\widehat H_{ii}} \sim \chi _i^2, \end{equation}

where ${\hat \theta _i}$ is one of the parameters in $\boldsymbol{\widehat {\theta\,} _{\!\! r}}$ and ${\widehat H_{ii}}$ is the ith parameter in the diagonal of the H matrix. The computational complexity of the LM and Wald tests depends on the number of free and fixed parameters in a model. As the number of parameters increases, estimating and comparing their effects becomes more computationally demanding. The H matrix, which represents the covariance matrix of the independent variables, reflects the number of parameters that can be either free or fixed. For a model with k independent variables, the H matrix will have ${k^2}$ elements (Chou and Huh, Reference Chou, Huh and Hoyle2012).

2.3. Evaluation of model fit

This study uses ML estimation, the standard method for deriving goodness-of-fit statistics and parameter estimates in CSA under normal theory. In CSA, a random sample, $x \in ${ ${x_1}, \ldots .,{x_n}$} is assumed to be independently and identically distributed, following a multivariate normal distribution $\mathcal{N}$[ $\boldsymbol{\mu} $, ${{\boldsymbol{\Sigma }}_0}]$. Here, $\boldsymbol{\mu} $ represents a vector of sample means, and the covariance matrix ${{\boldsymbol{\Sigma }}_0}$ is assumed positive definite with an unknown population parameter vector ${\boldsymbol{\theta} _0}$ of dimension $q \times 1$, where ${{\boldsymbol{\Sigma }}_0} = {\boldsymbol{\Sigma }}\left( {{\boldsymbol{\theta} _0}} \right)$. The sample covariance matrix is:

(8)\begin{equation}\boldsymbol{S} = {\text{ }}\frac{1}{{n - 1}}{\text{ }}\mathop \sum \limits_{i = 1}^n \left( {{x_i} - \bar x} \right){\left( {{x_i} - \bar x} \right)^{\prime}}\end{equation}

where the sample mean $\bar x = {\text{ }}\frac{1}{n}\displaystyle \sum_{i = 1}^n ({x_1}, \ldots .,{x_n})$. S serves as an unbiased estimator of the population covariance ${{\boldsymbol{\Sigma }}_0}$.

In confirmatory factor analysis (CFA), a model can be represented as:

(9)\begin{equation}\boldsymbol{x_i} = \boldsymbol{\mu} + \boldsymbol{\Lambda {\xi _i}} + \boldsymbol{\epsilon _i},\,\,i = 1,\, \ldots ,n\end{equation}

where $\boldsymbol{x_i}$ is a random sample, $\boldsymbol{\mu} $ is a sample mean vector, ${\boldsymbol{\Lambda }}$ is a matrix of factor loadings, $\boldsymbol{\xi _i}$ is a vector of latent factors, and $\boldsymbol{\epsilon _i}$ is a vector of residuals. Here, the parameters involved in a model are contained in the covariance matrix ${\boldsymbol{\Sigma }}$ of the observed variables. ${\boldsymbol{\Sigma }} = {\boldsymbol{\Lambda \Phi }}\boldsymbol{{{\Lambda }}^{\prime}} + {\boldsymbol{\Psi }}$, where ${\boldsymbol{\Lambda }}$ again is a factor loading matrix, and ${\boldsymbol{\Phi }}$ is a covariance matrix of the latent factors, and ${\boldsymbol{\Psi }}$ is a covariance matrix of unique scores.

The population covariance matrix ${\boldsymbol{\Sigma }}$ is modeled as ${\boldsymbol{\Sigma }}( \boldsymbol{\theta})$, where $\boldsymbol{\theta} $ contains free parameters ${\boldsymbol{\Lambda }}$, ${\boldsymbol{\Phi }}$, and ${\boldsymbol{\Psi }}$. The sample covariance matrix S serves as an unbiased estimator of ${\boldsymbol{\Sigma }}$, with the null hypothesis ${\boldsymbol{\Sigma }} = \boldsymbol{{\Sigma }( \theta)}$. An objective function ${F[ \boldsymbol{{{\Sigma }}( \theta ),{\text{ }}S}]}$ measures the discrepancy between $\boldsymbol{{\Sigma }( \theta)}$ and $\boldsymbol{S}$.

This study derives the goodness-of-fit statistic ${T_{ML}}$ using the ML discrepancy function (Jöreskog, Reference Jöreskog1969), fitting the model-implied covariance matrix $\boldsymbol{{\Sigma }( \theta)}$ to the sample covariance matrix S, as shown in Equation (10):

(10)\begin{equation}{F_{ML}}( \theta) = \log \left| {{\boldsymbol{\Sigma }}\boldsymbol{( \theta)} - {\text{log}}} \right|\!\boldsymbol{S}| + tr\left( {\boldsymbol{S}{\boldsymbol{\Sigma }}{{\boldsymbol{\left( {{\theta }} \right)}}^{ - 1}}} \right) - p\end{equation}

(11)\begin{equation}{\boldsymbol{\widehat {\theta\,}} _{\! ML}} = argmin{\text{ }}{F_{ML}}\left( \boldsymbol{\theta} \right)\end{equation}

The ML fit function ${F_{ML}}\left( \boldsymbol{\theta} \right){\text{ }}$ derives parameter estimates in $\boldsymbol{{\Sigma }( \theta)}$ that minimize the test statistic. At its minimum, as shown in Equation (11), ${\boldsymbol{\widehat {\theta\,}} _{\! ML}}$ contains parameter estimates $\boldsymbol{\widehat {{\Lambda }}}, \boldsymbol{\widehat {{\Phi }}}$, and $\boldsymbol{\widehat {{\Psi }}}$. Using these, we can reconstruct the sample covariance matrix to align with the model-implied covariance $\boldsymbol{{{\Sigma }}\left( {\widehat {{\theta\, }}} \right) = {\text{ }}\widehat {{\Lambda }}\widehat {{\Phi }}{\widehat {{\Lambda }}^{\prime}} + {\text{ }}\widehat {{\Psi }}}$, assuming the sample-implied matrix matches the population matrix. If $\boldsymbol{S \approx {{\Sigma }}\left( {\widehat {\theta\,} } \right)}$ with p-value > 0.05, the model is considered plausible.

The ML goodness-of-fit test statistic is calculated as:

(12)\begin{equation}{T_{ML}} = \left( {N - 1} \right){F_{ML}}\left( \boldsymbol{\widehat {\theta\,} } \right)\!.\end{equation}

This statistic is the product of $F_{ML}(\boldsymbol{\widehat{\theta\,}})$ and $\left( {N - 1} \right)$, where $N$ is sample size. As $N$ increases, ${T_{ML}}$ is expected to asymptotically follow a ${\chi ^2}$ distribution with corresponding degrees of freedom.

4.1. Population model and analysis model

The simulation begins with a hypothetical population model consisting of a three-factor structure, with each factor measured by eight manifest variables, as illustrated in Figure 2. An analysis model, shown in Figure 3, consists of 3 factors, each linked to 8 indicators, with all factors freely correlated, resulting in a total of 24 variables. The four dashed lines in Figure 2 represent the paths not included in the analysis model. Omitting four parameters aims to reduce the chance of the initial model closely resembling the true model. Including many missing parameters can result in a poorly specified model, potentially rendering LM test results meaningless or falsely indicating statistical significance by chance (Yuan et al., Reference Yuan, Marshall and Peter2003).

Figure 2.

Path diagram of the population model.

Figure 3.

Path diagram of the misspecified analysis model.

4.2. Monte Carlo simulations

The simulated data are generated using a standard confirmatory factor model, given by Equation (13):

(13)\begin{equation}\boldsymbol{{x_i} = \varLambda {\xi _i} + {\epsilon _i}}\end{equation}

where $\boldsymbol{x_i} = \left( {{x_{i1}},{\text{ }}{x_{i2}}, \ldots {x_{ip}}} \right)'$ is a vector of $p$ observations on person $i$ in a population, and $i = 1,2,\ldots n$. $\boldsymbol{\varLambda} $ is a matrix of factor loadings, and $\boldsymbol{\epsilon _i} = \left( {{\epsilon _{i1}},\,{\epsilon _{i2}},\, \ldots {\epsilon _{ip}}} \right)^{\prime} $ is a vector of error terms, and var $\left( \varepsilon \right) = \boldsymbol{\Psi} $. $\boldsymbol{\xi _i} = ({\xi _{i1,{\text{ }}}}{\xi _{i2, \ldots }}{\xi _{im}})'$ is a vector of latent factors, and var $\left( \boldsymbol{\xi} \right) = {\boldsymbol{\Phi }}$. Each latent factor $\boldsymbol{\xi _i}$ has a mean and a variance and may correlate with other latent factors $\boldsymbol{\xi _i}$; whereas $\boldsymbol{\xi _i}$ and $\boldsymbol{\epsilon _i}$ are uncorrelated, so that $E\left( \boldsymbol{\xi} \right) = {\boldsymbol{\mu} _\xi }$, which is the mean of the factors.

With the data generation scheme and population model described above, we simulate a population and draw samples using Monte Carlo simulation, based on the predefined matrices $\boldsymbol{{{\Lambda }}^{\prime}}$ and $\boldsymbol{{\Phi }}$:

\begin{align*}{\boldsymbol{{\Lambda }}^{\prime}}& =\left[ \begin{array}{cccccccccccc} {0.65}& 0.65 & 0.7 & 0.7 & 0.7 & {0.7} & 0.6 & 0.5 & 0.5 & 0 & 0 & 0 \\ 0& 0 & 0 & 0 & 0 & 0.5 & 0 & 0 & 0.6 & 0.6 & 0.6 &0.7 \\ 0& 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \end{array}\right.\\ &\qquad \left. \begin{array}{cccccccccccc} 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\ 0.7 & 0.5& 0.5 & 0.65&0&0 & 0& 0.65 &0 & 0&0&0\\ 0 & 0 & 0 & 0.45& 0.5 & 0.5 & 0.5 & 0.6 & 0.6 & 0.6 & 0.7 & 0.7 \end{array} \right]\!,\\ \boldsymbol{\Phi} &= \left[ \begin{gathered} 1 \hfill \\ 0.3\,\,\,\,\,\,\,\,1 \hfill \\ 0.4\,\,\,\,\,0.5\,\,\,\,\,1 \hfill \\ \end{gathered} \right] \end{align*}

When diag( ${\boldsymbol{\Sigma }}$) =  ${\textbf{{\textrm I}}}$, which is an identity matrix, the unique variances can be determined by ${\text{ }}{\boldsymbol{\Psi }} = {{\mathbf{I}}_{24}} - {\text{diag}}\left( {\boldsymbol{{\Lambda \Phi }}\boldsymbol{{{\Lambda }}^{\prime}}} \right)$. Since we are not interested in the mean structure, we set the factor means $\mu 's = \left( {0,{\text{ }}0,{\text{ }}0} \right)$. The data generating process consists of two steps. (1) We draw from a multivariate normal distribution with zero mean and covariance matrix $\boldsymbol{\Phi }$. Unique factors $\boldsymbol{\epsilon _i}$ are drawn from a multivariate normal distribution with zero mean and covariance ${\boldsymbol{\Psi }}$. Utilizing Equation (13), this procedure generates multivariate normal observations characterized by a covariance matrix $\boldsymbol{{{\Sigma }}\left( \theta \right)}$.

The data generation and all analyses for this research are conducted using the “lavaan” package (Version 4.2.3.) (Rosseel, Reference Rosseel2012) in R, based on the previously specified population and assuming multivariate normality. The simulation studies involved sample sizes of N = 100 to 10,000. Our testing models consisted of 24 observed variables (p = 24) and 3 latent factors, resulting in a covariance component of p* = 24(24 + 1)/2 = 300, with 55 free parameters to estimate, and 245 degrees of freedom. This model size is a good representation of most SEM research.

To assess the stability of the model specifications, we conduct a series of Monte Carlo simulations using the population model (Figure 2) across different sample sizes and fit the analysis model (Figure 3). The study includes 12 sample sizes—100, 150, 200, 250, 300, 350, 400, 500, 1,000, and 2,000—that are selected to reveal important phenomena related to the issues under study. To evaluate the goodness-of-fit, we employ various methods, including ${\chi ^2}$ test statistics, standard deviations of the ${\chi ^2}$ test, and the rejection rate. Additionally, we utilize alternative fit measures such as the comparative fit index (CFI), normed fit index (NFI), and root mean square error of approximation (RMSEA). However, as the analysis model does not fit the population model, we expect the ${\chi ^2}$ test statistic to be larger than the degrees of freedom and the p-value < 0.05.

4.3. Selection of testing parameters

To commence the specification search, an initial set of parameters is required. The process of selecting these parameters begins with an exploratory univariate LM test via model modification indices (Sörbom, Reference Sörbom1989). However, as the data are generated from Monte Carlo simulations, each sample drawn from the population model will be different, leading to variability in ${T_{LMi}}$. For instance, if we draw 500 samples of the same sample size, we will obtain 500 unique sets of initial testing parameters. Nonetheless, there should be a set of common parameters that frequently appear across all samples, including the true missing parameters. To obtain a more representative set of initial parameters, after we simulate the data for 500 trials, we calculate the mean ${T_{LMi}}{\text{ }}$of each parameter and sort them in descending order. We then select the top 12 parameters with the largest ${T_{LMi}}$ to test the proposed improved LM test. The LM test is designed to enhance the fit of the existing model, assuming it is reasonably well-fitted. Consequently, a sensible model should expect only a few significant omitted parameters. If the count exceeds 12, the model may suffer from severe misspecification issues, necessitating a new formulation.

4.4. Bootstrap simulation

The bootstrap method efficiently approximates population covariance structures in simulations, providing a practical alternative when the distribution of the sample is unknown. This approach tackles numerous challenges that conventional statistical methods encounter. For instance, the bootstrap approach does not assume normally distributed data. Even in cases where the data are normally distributed, at a given sample size, the bootstrap often provides more accurate results than those based on standard asymptotic methods (Yuan et al., Reference Yuan, Hayashi and Yanagihara2007).

Let ${x_1}$, ${x_2},\cdots, {x_n}$ denote a sample with a covariance matrix represented as $\boldsymbol{S}$ where its population counterpart is ${{\boldsymbol{\Sigma }}_0}$. The bootstrap method iteratively draws samples from a known empirical distribution function, effectively substituting it for the population in the bootstrap samples. However, since the population covariance matrix ${{\boldsymbol{\Sigma }}_0}$ is unknown, an alternative matrix $\boldsymbol{\skew2\dot{S}}$ must be found to serve as a surrogate for ${{\boldsymbol{\Sigma }}_0}$. Consequently, each ${x_i}$ can be transformed into $x_i^{\prime}$ by:

(14)\begin{equation}x_i^{\prime} = \boldsymbol{\skew2\dot{S}}^{1/2}{\boldsymbol{S}^{ - 1/2}}{x_i}, i = 1, 2, \cdots,n\end{equation}

where ${\boldsymbol{\skew2\dot{S}}}^{1/2}$ ${\boldsymbol{\skew2\dot{S}}}^{1/2}$ is a $p \times p$ matrix satisfying $\left( {{\boldsymbol{S}^{ - 1/2}}} \right){\left( {{\boldsymbol{S}^{ - 1/2}}} \right)^{\prime}}$ = ${\boldsymbol{\skew2\dot{S}}}$. The subsequent steps involve generating the bootstrap samples by sampling with replacement from $\left( {x_1^{\prime},{\text{ }}x_2^{\prime}, \cdots ,{\text{ }}x_n^{\prime}} \right)$, thereby computing the sample covariance matrix denoted as ${\boldsymbol{S}^*}$ for these bootstrap samples.

4.5. Bootstrap LM test

Chance-based model risks occur when different model fits arise from different samples of the same population, influenced by factors such as sample size, model complexity, and degrees of freedom. Significant chance-based model risks imply a higher likelihood of overlooking pathways within the model. To illustrate, Figure 4 visualizes the relationship between univariate LM tests, parameters, and various sample sizes. The x-axis represents the distribution of testing parameters, derived from 500 Monte Carlo simulations based on the population and analysis models depicted in Figures 2 and 3. On the other hand, the y-axis illustrates the univariate LM test statistics. Figure 4 highlights the true missing parameters. As observed, when the sample size is small, the distributions of LM tests for all parameters exhibit relatively large variations, including the true missing parameters. This phenomenon arises due to the small sample size relative to the model size and degrees of freedom. Consequently, the standard LM test faces challenges in distinguishing the true missing parameters from other parameters or effectively identifying any potential missing paths, presenting an issue due to its vulnerability to chance-based interpretations. However, as the sample size increases, the variation in the LM tests for the true missing parameters diminishes. This leads to a clearer distinction between the true missing parameters and other parameters, reducing the likelihood of being misled by noise.

Figure 4.

Univariate LM test statistics across varying sample sizes.

In the third stage of our analysis, we employ a forward stepwise approach along with bootstrap resampling to identify the optimal specifications. While statistically significant large LM test values are observed, they don’t necessarily imply that the suggested parameters accurately reflect the “true” values. This is because the data are generally a sample drawn from the larger population. Furthermore, in real-world data analysis, researchers frequently contend with finite sample sizes and unknown data distributions, giving rise to sample-specific errors and characteristics that may impact the model’s accuracy in reflecting the population. Consequently, the parameters recommended by the standard LM test might not generalize effectively to different samples. In this regard, bootstrap resampling can handle the issue of unknown distribution and provide more accurate results than those based on standard asymptotic properties.

In our approach, we use bootstrap resampling in every forward stepwise procedure. The LM test for a set of omitted parameters can be broken down into a series of 1- $df$ tests. Bentler and Dijkstra (Reference Bentler, Dijkstra and Kirshnaiah1985) developed a forward stepwise LM procedure, where at each step, the parameter is chosen that will maximally increase the LM ${\chi ^2}$. We will perform the bootstrap LM test by examining two parameters at a time. At each step, we randomly draw 500 samples with replacements and compute the mean LM test statistic and its p-value. We will repeat this process by adding another pair of parameters until we have tested all possible omitted parameters in the analysis model.

It is crucial to perform multiple repetitions of the test during this process as the LM ${\chi ^2}$ value can vary depending on the model parameters and their correlations with each other. Adding or removing parameters will change the covariance structure for the LM test, and hence, the LM test statistic at each step will provide more accurate information about the remaining missing parameters. We designate the parameters with p-values less than 0.05 as statistically significant and refer to them as bootstrap LM-based parameters for the Wald test selection.

4.6. Bootstrap Wald test

The bootstrap LM tests establish a set of parameters for subsequent validation, while the Wald tests incorporate these recommended parameters and conduct a series of backward stepwise bootstrap Wald tests. The Wald tests assess whether each initially treated-as-free parameter can be collectively set to zero without a significant loss in model fit. This simplifies the model by removing nonsignificant parameters and provides further validation. Parameters that are truly missing exhibit p-values < 0.05, confirming their significance and justifying their inclusion in the model. Conversely, parameters that should be excluded from the model yield p-values ≥ 0.05. This integrated approach effectively addresses the problem of false positives.

5.1. Model specification stability and model fit validity

In this section, we aim to assess the stability of the improved LM test-suggested model fit over repeated samples of different sample sizes. A model that fits the data well should follow a standard ${\chi ^2}$ distribution, ${T_{ML}}\mathop \to \limits^{\mathcal{L}} \chi _{df}^2$, as N grows larger, demonstrating asymptotic properties (Browne, Reference Browne1984; Bentler and Dijkstra, Reference Bentler, Dijkstra and Kirshnaiah1985; Jöreskog and Sörbom, Reference Jöreskog and Sörbom1988). Based on this reasoning, we fit the improved LM test-suggested model to the simulated data drawn from the population model (Figure 2). If the ${T_{ML}}$ are close to the degrees of freedom, it provides strong empirical evidence that the improved LM test-suggested model fits better. To ensure its generalizability, we randomly draw samples of different sizes from the population model and fit the improved LM test-suggested model. If consistency is maintained, we are confident that the improved LM test-suggested model is adequate for general use.

The Monte Carlo simulations in this study are based on Equation (13). We conduct 1,000 trials and calculate the average statistics, which are reported in Table 2. We examine the performance of the analysis model suggested by the improved LM test by varying the sample sizes from 100 to 10,000. Since the simulated data are normally distributed, the ML estimator is sufficient to examine the basic statistical performance and asymptotic properties.

Table 2.

Monte Carlo simulation results for asymptotic properties

Table 2 presents the mean χ ² test statistics, their mean standard deviations, mean p-values, mean rejection rates, and the 2.5th and 97.5th percentiles of fit indices (NFI, CFI, and RMSEA) by sample size. As shown in Table 2, as the sample size increases, all mean statistics test statistics get closer to the expected values: ${\chi ^2}$ = 269, SD = 23.195, p-value = 0.50, and empirical rejection rate is 0.05. Note that when sample sizes are less than 500, the ${\chi ^2}$ test statistics are increasingly inflated, deviating from the expected value of 245. As documented by previous studies, ML estimator is biased against small sample sizes (Arruda and Bentler, Reference Arruda and Bentler2017; Hayakawa, Reference Hayakawa2019; Zheng and Bentler, Reference Zheng and Bentler2021, Reference Zheng and Bentler2023).

In addition, NFI and CFI become closer to 1, and RMSEA is about 0 when the sample sizes are greater than 200. The collective statistical indicators provide compelling evidence that the improved LM test effectively detected the omitted parameters in the analysis model and provided a satisfactory fit to the simulated data. Moreover, the modified model, derived from the specification search results using the improved LM test, exhibited a robust statistical fit across various sample sizes.

5.2. Robustness of the improved LM test

To test the robustness of the improved LM test, we vary the magnitudes of factor correlations, the number of indicators per factor from low to high, and factor loadings. First, we find that with more indicators per factor, it becomes easier to detect omitted parameters. When the number of indicators per factor is fewer, the statistical power of the improved LM test is weakened, particularly with smaller sample sizes. Nevertheless, the improved LM test still outperforms the LRT across all sample sizes. When the number of indicators per factor increases, both the improved LM test and LRT perform similarly.

Second, when factor correlations are low, the improved LM test becomes more efficient at detecting omitted parameters. The performances of the improved LM test and the LRT become similar when sample sizes exceed 100. However, with smaller sample sizes, the improved LM test consistently outperforms the LRT. In contrast, when factor correlations are high, increasing potential relationships among factor loadings and residuals, the improved LM test continues to deliver outstanding performance.

Third, the magnitudes of factor loadings influence the performance of the improved LM test, and this is dependent on the sample size. When N ≥ 400, the improved LM test delivers efficient and robust performance compared to the LRT. However, low factor loadings in smaller sample sizes tend to have a stronger impact on the detection of omitted variables and convergence. We found that when N < 400, the models encounter convergence issues, mainly because the covariance matrix becomes not positive definite. In contrast, with high factor loadings, both the improved LM test and LRT perform well across all sample sizes in this study. Nonetheless, the improved LM test consistently outperforms the LRT in detecting correct parameters. For the results of the simulation tests, please refer to the Appendix.

6.1. Example 1: national identity and patriotism

In this study, we evaluate the effectiveness of the improved LM test using a covariance structure model constructed from Huddy and Khatib’s (Reference Huddy and Khatib2007) student data, gathered in 2002. For detailed data collection and student sample information, please refer to page 66 in Huddy and Khatib (Reference Huddy and Khatib2007). This dataset comprises 341 respondents. The survey questions use a 4-point Likert scale, with response options ranging from “strongly approve” to “strongly disapprove.” We employ the diagonally weighted least squares (DWLS) estimator to handle the ordered categorical variables. For brevity, the indicators and factors are unlabeled here; please refer to the appendix for the survey questions.

The path diagram for the three-factor model is depicted in Figure 5. This model involves a small sample size (N = 341) relative to a larger number of degrees of freedom ( $df$ = 39). The national identity and patriotism model consists of three latent factors, ${{\text{F}}_1},{\text{ }}{{\text{F}}_2}$, and ${{\text{F}}_3}$. These factors represent the constructs of national identity, symbolic patriotism, and uncritical patriotism, respectively. Each of these constructs is assessed by a series of indicators ${X_i}$. Factor loadings are denoted by ${\lambda _i}$, residuals by ${\varepsilon _i}$, and the residuals of factor by ${D_i}$. The coefficients ${\beta _1}$, ${\beta _2}$, and ${\beta _3}$ measure the correlations between the three factors. To evaluate the performance of the improved LM test, we follow the same procedure as employed in the simulated data. The dashed lines in Figure 5 represent the recommended parameters suggested by the improved LM test.

Figure 5.

Path diagram of national identity and patriotism (Huddy and Khatib, Reference Huddy and Khatib2007).

Table 3 displays the outcomes of three distinct tests: the univariate LM test, bootstrap LM test, and bootstrap Wald test. The bootstrap LM test, executed through a forward stepwise approach, identified five missing parameters that were statistically significant. The highlighted items indicate the actual missing parameters, and the items in bold in the bootstrap LM and Wald tests are statistically significant. The bootstrap Wald test concurred that the parameter ${\lambda _m}$ (the factor loading linking ${{\text{F}}_1}$ and q27) should be included in the original model to improve the model fit. q27 inquires, “How angry does it make you feel, if at all, when you hear someone criticizing the United States?” Response options range from extremely angry to not at all. Furthermore, the standardized factor loading of ${\lambda _m}$ is 0.47 and statistically significant. This indicates that differing levels of national identity ( ${{\text{F}}_1}$) and uncritical patriotism ( ${{\text{F}}_3}$) are likely to influence feelings of anger. While the addition of this parameter may not alter the overall substantive conclusion, it provides new insights into the nuances of these latent structural relationships. However, without including this parameter, Huddy and Khatib’s (Reference Huddy and Khatib2007) original model suffers from some degree of misspecification.

Table 3.

Summary of example 1 test statistics

Table 4 presents the results of our replication study based on Huddy and Khatib’s (Reference Huddy and Khatib2007) research using the DWLS estimator. In the original study, the ${\chi ^2}$ statistic is 65.176 with 40 degrees of freedom, resulting in a p-value of 0.007. The CFI is 0.997, NFI is 0.992, TLI is 0.996, and the RMSEA is 0.043. The ${\chi ^2}$ test statistic provides limited support for the substantive argument. However, upon introducing the omitted parameter, ${\lambda _m}$, as suggested by the improved LM test, the ${\chi ^2}$ test statistic reduces to 27.534, resulting in a p-value of 0.532. The improvement in the ${\chi ^2}$ test statistic is crucial as it suggests that the model-implied covariance structure is highly consistent with the sample covariance structure. Moreover, the NFI increases to 0.996, the CFI and TLI increase to 1.0, and the RMSEA decreases to 0. The final column in Table 4 indicates the differences in test statistics and fit indices between the two models, showcasing a significant enhancement in model fit and strengthening the theoretical argument based on this structural relationship.

Table 4.

Comparison of test statistics and model fit in example 1

6.2. Example 2: SEM of relationship of human value priorities

In another empirical application, we conducted an analysis of the German sample (N = 2919) from the 2002 European Social Survey, which is a cross-national probability survey. To illustrate the effectiveness of our improved LM test in small sample sizes, we randomly selected 500 observations from the German sample. Detailed information on data collection procedures and original survey questions can be found on the ESS website.

To analyze the data, we employed a four-factor SEM model, as depicted in Figure 6, following standard practice. The model comprises four latent factors represented by ovals, each measured by multiple indicators. In the original model, factor ${{\text{F}}_3}$ is predicted by factors ${{\text{F}}_1}$ and ${{\text{F}}_2}$, while factor ${{\text{F}}_4}$ is predicted by factors ${{\text{F}}_1}$ and ${{\text{F}}_2}$. Furthermore, there are correlations between factors ${{\text{F}}_1}$ and ${{\text{F}}_2}$, as well as ${{\text{F}}_3}$ and ${{\text{F}}_4}$. To evaluate the effectiveness of our proposed improved LM test approach, we removed the coefficient parameters between ${{\text{F}}_2}$ and ${{\text{F}}_3}$, and between ${{\text{F}}_3}$ and ${{\text{F}}_4}$, as indicated by the dash lines in Figure 6.

Figure 6.

SEM of human value priorities (Davidov, Reference Davidov2009; Oberski, Reference Oberski2014).

Note: Error and factor variances are not shown in the path diagram.

Table 5 presents the results of three tests conducted on the model: the LM test, bootstrap LM test, and bootstrap Wald test. The LM test identified the top 10 omitted parameters based on LM test statistics, out of which 2 parameters ( ${{\text{F}}_2}$, ${{\text{F}}_3}$) and ( ${{\text{F}}_3}$, ${{\text{F}}_4}$) were the actual missing parameters. The improved LM test confirmed the validity of these two parameters and identified four additional parameters (highlighted in bold in the first column in Table 5) from the ten tested in the model. Note that (F₁, F₃) is meaningless here because the original research aims to use a unidirectional arrow to indicate the effect of ${{\text{F}}_1}$ on ${{\text{F}}_3}$ based on their theoretical argument, while the improved LM test suggests a correlation instead. Thus, we disregard this suggested omitted parameter. The suggested parameter (F3, Understand) suggests that attitudes toward immigration may also be influenced by the universalism value, which emphasizes understanding and concern for the welfare of all people, as well as the influence of self-transcendence. Additionally, the suggested parameter (Equality, Tradition) indicates that the belief in treating every person equally is correlated with the value placed on tradition. Naturally, this relationship may vary significantly across different countries and cultures. Such variations could introduce measurement invariance and model misspecification issues when comparing the effects of values on attitudes toward immigration without accounting for these structural relationships.

Table 5.

Summary of example 2 test statistics

To determine whether adding these suggested parameters could improve the model fit, we compared the test statistics and fit indices. Table 6 reports that the original model’s ${\chi ^2}$ test is 324.224 with 115 degrees of freedom. However, when we added all the suggested parameters to the model, the ${\chi ^2}$ test decreased to 167.902, a reduction of 156.322, with a loss of only 6 degrees of freedom. Additionally, the NFI increased from 0.844 to 0.919, TLI increased from 0.872 to 0.962, and the CFI increased from 0.892 to 0.970, and the RMSEA decreased from 0.064 to 0.035. All these statistics confirmed that the improved LM test method yielded favorable results for this model by incorporating the additional parameters it recommended.

Table 6.

Comparisons of test statistics and fit indices

In summary, the improved LM test effectively identified omitted parameters in empirical examples 1 and 2. Incorporating these parameters, as shown in Tables 4 and 6, substantially improves the overall model fit in the ${\chi ^2}$ test statistics and fit indices. While these additions may not alter the substantive conclusions, they enhance confidence in the authors’ arguments and provide new insights into their theoretical claims. It is important to note, however, that although the improved LM test is a valuable data-driven method for uncovering hidden parameters, the decision to include suggested parameters should be guided by strong theoretical justification.