2.1 Dimensionality of the school climate questionnaire

We explored the dimensions of the item responses with a principle component analysis (PCA) of the correlation matrix. We applied the PCA to the data from all schools (see details of school-level dimensionality analysis in the Supplementary Material) to increase our sample size. Although a multigroup factor model or a multilevel factor model may be more appropriate, we considered a PCA as sufficient for an exploratory analysis, given that the inter-school differences were rather small (see the Supplementary Material for a comparison of four schools). The eigenvalues showed that there might be 3 or 4 components. The eigen values were $4.811 (28\%), 2.052 (14\%), 1.521 (9\%)$ , and $1.060 (7\%)$ for the first four components, respectively.

Given that there are three subscales in the questionnaire, we rotated the first three components to a theoretical target structure that corresponds to the three subscales (with a zero loading on the components that do not correspond to the subscale an item belongs to). All items of the same subscale were given the same high loadings in the target theoretical structure (i.e., a value of two different high values yield the same rotation result). The rotation was performed using Michael Browne’s target rotation, which is also implemented in CEFA (Browne et al., Reference Browne, Cudeck, Tateneni and Mels2008), using the target.rot function in the psych R package (Revelle & Revelle, Reference Revelle and Revelle2015).

The loadings of the rotated components are shown in Table 1. The loadings that are expected to be high based on the theoretical structure are in bold. The rotated components showed a clear correspondence with the theoretical structure, with the items from the same subscale loading on the same component and having low loadings on the other two components. To assess the similarity between the rotated components and the theoretical structure, we also calculated the congruence coefficients (see the results in Table 2). The congruence coefficients are the cosines between the rotated components and the components defined by the theoretical structure. Table 2 shows that the congruence coefficients are high between the corresponding components and the theoretical structure. Therefore, we believe that the three rotated components correspond to the three subscales. We will apply the same target rotation to the results from the joint analysis of the network data and questionnaire data in the application section. The joint model will be described next.

Table 1

Rotated loadings of the PCA

Table 1 Long description

The table consists of 16 rows labeled Item 1 through Item 16 and three columns labeled Student, Policy, and Satisfaction. For each item, the corresponding loadings are listed. High loadings, as expected from the target matrix, are bolded. Item 1: Student 0.667, Policy 0.049, Satisfaction 0.091. Item 2: Student 0.731, Policy negative 0.003, Satisfaction 0.146. Item 3: Student 0.291, Policy negative 0.051, Satisfaction negative 0.062. Item 4: Student 0.563, Policy 0.076, Satisfaction 0.045. Item 5: Student 0.767, Policy negative 0.071, Satisfaction negative 0.158. Item 6: Student negative 0.054, Policy 0.591, Satisfaction 0.159. Item 7: Student 0.056, Policy 0.621, Satisfaction 0.014. Item 8: Student negative 0.018, Policy 0.881, Satisfaction negative 0.107. Item 9: Student negative 0.045, Policy 0.866, Satisfaction negative 0.155. Item 10: Student 0.032, Policy 0.736, Satisfaction negative 0.107. Item 11: Student 0.036, Policy 0.556, Satisfaction 0.164. Item 12: Student negative 0.007, Policy 0.630, Satisfaction 0.188. Item 13: Student 0.084, Policy 0.127, Satisfaction 0.671. Item 14: Student negative 0.132, Policy 0.034, Satisfaction 0.866. Item 15: Student negative 0.085, Policy negative 0.031, Satisfaction 0.922. Item 16: Student 0.183, Policy 0.001, Satisfaction 0.672. The note below the table states that high loadings expected from the target matrix are bolded.

Note: The loadings that are expected to be high based on the target matrix are in bold.

Table 2

Congruence coefficients

Table 2 Long description

The table has four columns: Coefficients, Student, Policy, and Satisfaction. The first row lists the column headers. The first column lists the row headers: Student, Policy, Satisfaction. In the Student row, the values are 0.944 under Student, 0.000 under Policy, and 0.017 under Satisfaction. In the Policy row, the values are 0.000 under Student, 0.980 under Policy, and 0.035 under Satisfaction. In the Satisfaction row, the values are 0.017 under Student, 0.036 under Policy, and 0.953 under Satisfaction. Diagonal cells have the highest values, indicating strong self-congruence, while off-diagonal values are near zero.

3.1 Modeling the network component

In a latent space model, a node’s information is summarized by low-dimensional continuous latent variables and can sometimes be visually represented in a low-dimensional space. The latent variable value for a node can be written in vector form with the number of elements in the vector equaling the number of dimensions. We restrict the scope of our discussion to vector latent space models that use the vector product to model the connections between nodes (see Wang, Reference Wang2021 for details about vector latent space models). One reason for using vector latent space models is to maintain consistency with the item response component of the joint framework. Models for item responses often use the vector product between a latent person variable and an item slope in the item response function, for example, the two-parameter IRT model and the factor models (De Boeck & Wilson, Reference De Boeck and Wilson2004).

We can summarize the relational information of person a as a sender in the network using a K-dimensional vector $\boldsymbol {u}_a$ , called the latent position of sender a. Similarly, we can summarize the relational information of person b as a receiver in the network using a K-dimensional vector $\boldsymbol {v}_b$ , called the latent position of receiver b. The latent position for a node is the coordinate of the node on the corresponding latent network dimension. In this article, we model the probability of a connection from a sender a to a receiver b using the vector product $\boldsymbol {u}_a^T\boldsymbol {v}_b$ , called the multiplicative UV effect. In network analysis, other forms of the vector products also exist, for example, the bilinear effect (Hoff, Reference Hoff2005). The multiplicative UV effects model allows for separate sender and receiver role specific latent variables for a node and is therefore more suitable for a directed network. For a directed network with a low level of reciprocity, the multiplicative UV effect is a better choice than the bilinear effect. We use the multiplicative UV effect to model the directed relationships in the advice-seeking network.

3.1.1 Continuous edge weights

We introduce the vector latent space model for a network with continuous quantitative edge weights. Let $\boldsymbol {X}$ be an $N \times N$ adjacency matrix representing the network, where N is the number of persons in the network. The edge value $X_{a,b}$ represents the relationship from sender a to receiver b, $a,b =1,\ldots ,N$ and $b \neq a$ . For a directed network with continuous edge weights, the latent space model can be written as

(3.1)

where $\delta $ is a fixed intercept parameter accounting for the density of the network. As mentioned before, $\boldsymbol {u}_a$ and $\boldsymbol {v}_b$ are latent positions for sender a and receiver b. The error for the edge from sender a to receiver b is $e_{a,b}$ , and its covariance structure with $e_{b,a}$ follows Equation (3.1). We call $\sigma ^2_e$ the error variance and $\rho $ the within-dyadFootnote ² correlation. The within-dyad correlation accounts for reciprocity, that is, the degree to which relationships are reciprocated. When there is reciprocity, $X_{a,b}$ and $X_{b,a}$ are more likely to be the same. This model is identical to keeping only the multiplicative component of the additive and multiplicative effects (AME) model of Hoff (Reference Hoff2021). We use $\boldsymbol {U} $ and $\boldsymbol {V} $ to denote the $N \times K$ matrices of the coordinates on the latent network dimensions for sender and receiver, and $\boldsymbol {E}$ to denote the $N \times N$ matrix of network errors. The approximation of the posterior distributions of the unknown quantities is facilitated by setting prior distributions an $N(0, \sigma ^{-2}_0)$ with $\sigma ^{-2}_0>0$ for $\delta $ , a $\text {Unif}(-1, 1)$ for $\rho $ , and a $\text {gamma} (1/2, 1/2)$ for $\sigma _e^{-2}$ . The prior for the covariance of the latent network dimensions is described in the joint component.

Random node-specific intercepts or node specific additive effects can be added to the above network model (Hoff, Reference Hoff2021; Krivitsky et al., Reference Krivitsky, Handcock, Raftery and Hoff2009). In this case, Equation (3.1) will be modified to read $X_{a,b} = \delta + \eta _a + \xi _b + \boldsymbol {u}_a^T \boldsymbol {v}_b + e_{a,b}$ , with a suitable correlation structure on $\eta _a, \xi _b,\boldsymbol {u}_a$ , and $\boldsymbol {v}_b$ . Karrer and Newman (Reference Karrer and Newman2011) make a case for modeling the heterogeneity of the node degrees with additive node-level (fixed) effects in stochastic blockmodels, resulting in degree-corrected stochastic blockmodels (see discussions about the additive node-level effects in stochastic blockmodels in Jin, Reference Jin2015 and Zhao et al., Reference Zhao, Levina and Zhu2012). Without the additive node-level effects, blockmodels tend to identify clusters based on node degrees (e.g., a cluster of active nodes versus a cluster of less active nodes), especially when there is large heterogeneity in the node degrees. However, it is less clear if the additive node-level effects, for example, the random row and column intercepts $\eta _a, \xi _b$ , are needed to model all types of networks in latent variable models. Indeed, the additive effects are useful to capture higher (or lower) nodal activity directed to all nodes in the network irrespective of their positions in the latent space. On the other hand, the length of the latent variable of the node contributes to the activity level of the node in relation to other nodes in the space—this is captured by the multiplicative nature of the vector products to model connections. In small data sets with moderate levels of heterogeneity of the node degrees, the latent positions alone might be sufficient for modeling the activity levels of the nodes. Although, in principle, we can expect improvement in the recovery of the node degrees with added additive effects, a situation might arise where the improvement is negligible.

In this article, we choose not to include additive effects in the network component of the framework. In our application of the advice-seeking network and the school climate questionnaire, we observe comparable recovery of node degrees as well as comparable model fit with and without the nodal effects in the model. Therefore, we think the nodal effects are not needed in the modeling of the advice-seeking network though this might not be the case for other networks.

Although the proposed approach for the network component of the joint framework can be seen as an AME model, the difference of our approach is that we connect the network component with the item response component using a covariance-based joint component. See details about the joint component in Section 3.3.

3.2 Modeling the item response component

To model the school climate questionnaire, we propose a latent variable model with vector products for continuous observations. Through this methodology, we aim to get three sets of information: (1) item intercept, (2) item slope, and (3) the latent variable values. The item intercept reflects the average of the responses across all persons for each item. A higher intercept indicates a higher average response. The item slope reflects how well the item differentiates in terms of the latent variable. One unit difference in the latent variable results in a large difference in the probabilities of response when the item slope is large.

Consider the continuous item responses, $\boldsymbol {Y}$ , an $N \times M$ matrix, where N is the number of persons and M is the number of items. The response, $Y_{p,i}$ , represents person p’s response to item i, $p = 1,2,\ldots , N$ and $i = 1,2,\ldots , M$ . Let D be the number of dimensions or latent variables in the item responses. The model can be written as

(3.2) $$ \begin{align} Y_{p,i} = \beta_i + \boldsymbol{\alpha}_i^T \boldsymbol{\theta}_p + \epsilon_{p,i}, \qquad \boldsymbol{\theta}_p \overset{iid}{\sim} \text{MVN}(0, \Lambda_{\theta}) , \qquad \epsilon_{p,i} \overset{iid}{\sim} N(0, \sigma^2_{\epsilon}), \end{align} $$

where $\boldsymbol {\alpha }_i$ and $\boldsymbol {\theta }_p$ are the D-dimensional vectors containing the item slopes and the person latent variables of the item responses; $\beta _i$ is the intercept of item i; and $\sigma ^2_{\epsilon }$ is the error variance for the item responses. As is common in models for item responses, the parameters for the items (including the item slope, $\boldsymbol {\alpha }_i$ and the item intercept, $\beta _i$ ) are fixed, and the person variable, $\boldsymbol {\theta }_p$ are random. We use $\boldsymbol {\beta } $ to denote the $M \times 1$ vector of item intercepts, $\boldsymbol {A}$ to denote the $M \times D$ matrix of item slopes, $\boldsymbol {\Theta }$ to denote the $N \times D$ matrix of latent variables of item responses, and $\boldsymbol {\Psi }$ to denote the $N \times M$ matrix of item response errors. Let us define $\boldsymbol {\xi }_i$ as a vector of item parameters, $ \boldsymbol {\xi }_i = (\boldsymbol {\alpha }_i^T, \beta _i)^T$ . Approximation of the posterior distribution of the item parameters is facilitated by setting a $\text {MVN} (\boldsymbol {\mu }_{\xi }, \boldsymbol {\Sigma }_{\xi }), \boldsymbol {\mu }_{\xi } = (1,1,\ldots ,1,0)^T, \boldsymbol {\Sigma }_{\xi } = \boldsymbol {I}_{D+1} $ prior distribution for $\boldsymbol {\xi }_i$ . We set a prior distribution of $\text {gamma } (1/2, 1/2)$ for $\sigma _{\epsilon }^{-2}$ . The prior for the covariance of the latent variables of the item responses is described in the joint component. In the case of binary item response, similar to binary network edge value, we can model the binary network response as the binary indicator that the latent continuous measure, $\eta _{p,i}$ is larger than zero, that is, $Y_{p,i} = 1$ , when $\eta _{p,i}>0$ .

Identifiability: The latent variables of the item responses and the item slopes, $\boldsymbol {\Theta }$ and $\boldsymbol {A,}$ are identified up to rotations while $\boldsymbol {\Theta } \boldsymbol {A}^T$ is directly identified following the condition that either $\boldsymbol {\Theta }$ or $\boldsymbol {A}$ is centered. Following convention, we focus on the condition that $\boldsymbol {\Theta }$ is centered.

The proof of Lemma 2 is in the Supplementary Material. In addition to the condition identified above, we also fix the latent variables to have unit variances and orthogonality between dimensions following the current factor model (and item response model) literature. Similar to the network component of the JNIRM, we also use the first D left and right singular vectors of the posterior means of the vector products as the estimates of $\boldsymbol {\theta }$ and $\boldsymbol {\alpha }$ . In this approach, only the convergence of the vector products is needed for the estimation of the item slopes and the person variables, not the convergence of the item slopes and the latent variables themselves.

3.3 Modeling the joint component

Recall that the network component of the joint framework is modeled following Equation (3.1), and that the item response component of the joint framework is modeled following Equation (3.2). In these two components, we use normal distributions for the network dimensions and the latent variables of the item responses separately. To connect these two sets of variables, we use a multivariate normal distribution with a joint covariance matrix:

(3.3)

In Equation (3.3), we use $\boldsymbol {\Sigma }_{u\theta }$ to denote the omnibus covariance matrix capturing the co-varying relationships between network and item responses, which can be partitioned into four blocks, $\Lambda _u$ , $\Lambda _{u\theta }^T$ , $\Lambda _{u\theta }$ , and $\Lambda _{\theta }$ with $\Lambda _{u\theta }^T$ being the transpose of $\Lambda _{u\theta }$ containing the same parameters. In this notation, for simplicity and to reduce the complexity of notations, we use subscript u to denote the network component, omitting subscript v, and subscript $\theta $ to denote the item response component. Approximation of the posterior distribution of $ \boldsymbol {\Sigma }_{u\theta } $ is facilitated by setting a prior distribution of $ \text {Wishart} (\boldsymbol {I}_{2K+D}, 2K+D+2)$ . The covariance of the latent network dimensions $(\boldsymbol {u}_p, \boldsymbol {v}_p)^T$ is the $2K \times 2K$ matrix $\boldsymbol {\Lambda }_{u}$ , and the covariance of the latent variables of the item responses is the $D \times D$ matrix $\boldsymbol {\Lambda }_{\theta }$ . If the network dimensions are independent, then $\boldsymbol {\Lambda }_{u}$ is a diagonal matrix. If the dimensions of the item responses are independent, then $\boldsymbol {\Lambda }_{\theta }$ is a diagonal matrix. In JNIRM, we can test whether the network and item responses are dependent by testing whether the $D \times 2K$ matrix $\boldsymbol {\Lambda }_{u\theta } = 0.$ See details in the next section. The model is estimated using a Gibbs sampler outlined in Section 4.

3.4 Advantages

The superiority of JNIRM includes (1) more realistic model representation of the mutual impact of social relationship and school climate, (2) flexible accommodations of missing observations, and (3) cost effective study designs that improve precision of inference without collecting additional data. First, unlike regression models that assume one-directional relationships between advice-seeking networks and three school climate dimensions,—assuming that school climate dimensions impact teachers’ advice-seeking behaviors or vice versa—JNIRM acknowledges the mutual relationship between them. Changes in the school climate often correlate with changes in advice-seeking networks, but teachers’ relationships with each other can also influence school climate. In this way, JNIRM can offer a more realistic representation of the relationship between the advice-seeking networks and school climate, allowing for a more flexible interpretation of the results. Second, network regression models with school climate as covariates assume that each teacher’s school climate score is available (covariates are assumed to be fixed observations in a regression model) and that the school climate data are fully observed (no missing observation can be accommodated in covariates of a regression model). This assumption becomes problematic when data include missing observations for teacher’s school climate scores; in such situations, these teachers’ information may be discarded from the model, or additional data processing procedures for missing data are warranted, which often comes with their own modeling assumptions and inconsistencies. Regression methods struggle to handle situations where data are incomplete when incorporating school climate information as covariates. Lastly, by simultaneously modeling the reciprocal influence between networks and behaviors in a shared data generation process, we are able to improve model performance and prediction accuracy in certain situations where networks are sparse. During the MCMC sampling, the estimation of the latent network variables is not only informed by the network data, but also by the school climate item responses. In this way, without collecting additional data with more sample size, we are able to increase the amount of information in estimating network parameters, leading to more powerful and cost-effective studies.

3.5 Exploring the dependence between the network and item responses

In this section, we discuss how to make inferences regarding the dependence between the network and item responses. Due to the previously described solution for the identification issues, we cannot directly determine uncertainty about the covariance matrix based on the MCMC chain. Instead, following Fosdick and Hoff (Reference Fosdick and Hoff2015), we use the multivariate test of independence and the CCA to study the dependence between the two components of the joint framework. We first discuss the test and then CCA.

3.5.1 The multivariate test of independence

The classical multivariate test of independence (see Anderson, Reference Anderson1962) is applied using the estimated network dimensions and the latent variables of the item response. More specifically, the null and the alternative hypotheses of the test are

(3.4) $$ \begin{align} H_0: \Lambda_{u\theta} = 0 \qquad \text{vs.} \qquad H_1: \Lambda_{u\theta} \neq 0. \end{align} $$

The test statisticFootnote ³ is $t = \frac {\max _{ \Lambda _u, \Lambda _{\theta }} L_0(\Lambda _u, \Lambda _{\theta } | \boldsymbol {u}_p, \boldsymbol {v}_p, \boldsymbol {\theta }_p) }{ \max _{\boldsymbol {\Sigma }_{u\theta }} L(\boldsymbol {\Sigma }_{u\theta }|\boldsymbol {u}_p, \boldsymbol {v}_p, \boldsymbol {\theta }_p)} = \left ( \frac { |\hat {\boldsymbol {\Sigma }}_{u\theta }|}{|\hat {\Lambda }_u| \cdot | \hat {\Lambda }_{\theta }|} \right )$ , where $L_0$ and L are the likelihood with and without restricting $\Lambda _{u\theta }=0$ , and estimates of the covariance matrix are obtained using the estimated latent network dimensions and the latent variables of the item responses. The critical region of the test is $t < t(\alpha )$ , where $t(\alpha )$ is the threshold value such that the probability of an observed t is smaller than $t(\alpha )$ with probability $\alpha $ .

3.5.2 Canonical correlation analysis

Following the rejection of the null hypothesis in the classical multivariate test of independence between the network and item responses, we may wish to know where the lack of independence lies. We can use the CCA to achieve this goal. In this section, we provide a brief overview of CCA, and we focus on how it can be applied to understand the relationship between the network and item responses. For more detailed discussions about this topic, we refer readers to Anderson (Reference Anderson1962), Marden (Reference Marden2015), Sherry and Henson (Reference Sherry and Henson2005), and Thorndike (Reference Thorndike2000).

CCA is a useful technique to study the relationship between two sets of variables. In this case, the first set of variables includes the network dimensions from the network component of the JNIRM, and the second set of variables includes the latent variables from the item response component of the JNIRM. Using CCA, we estimate the largest correlation obtainable between a linear combination of the network dimensions and a linear combination of the latent variables of the item responses. Then, a second largest correlation is obtained with linear combinations that are uncorrelated with the first linear combinations. The process is repeated, with each set of linear combinations maximizing the correlation subject to being uncorrelated with the previous combinations.

In Figure 3, we demonstrate CCA with a hypothetical JNIRM when the number of dimensions for the network is two, and the number of dimensions for the item responses is three. In the left side of the figure, the latent variables of the item responses are linearly combined to create the item response canonical function, and a similar process occurs in the right side of the figure for the network. The weights of the linear combination are called the canonical weights or the function coefficients, and the resulting weighted sum is called the canonical function. For example, an item response canonical function is the weighted sum of the latent variables of the item responses. There can be as many canonical functions as the smallest set of variables at one of the two sides. The canonical functions are orthogonal to each other following the independence of the linear combinations. We will base our interpretations with CCA on standardized function coefficients.

Figure 3

Illustration of the network and item response canonical functions in a canonical correlation analysis with three latent variables from the item responses and two network dimensions. The canonical correlation is the correlation between the network and item response canonical functions, which are linear combinations of the item response latent variables and the network dimensions.

Figure 3 Long description

On the left, three stacked rectangles labeled item response latent variable 1, item response latent variable 2, and item response latent variable 3 each have arrows pointing right to an oval labeled item response canonical function. This oval is connected by a double-headed arrow labeled canonical correlation R to a second oval labeled network canonical function. From this second oval, four arrows extend rightward to four stacked rectangles labeled network sender dimension 1, network sender dimension 2, network receiver dimension 1, and network receiver dimension 2. The diagram illustrates the mapping of three item response latent variables into a canonical function, which is then correlated with a network canonical function composed of two sender and two receiver dimensions.

In the center of Figure 3, the canonical correlation, $R,$ is shown as the correlation between the network and the item response canonical functions. The square of the canonical correlation, $R^2$ , represents the proportion of variance shared by the network and the item response canonical functions. The square of the second canonical correlation indicates the proportion of the shared variance between the second pair of canonical functions based on the remaining relationship (independent of the first pair of canonical functions).

Significance tests can be conducted in association with the canonical correlations. Testing each correlation separately for statistical significance is not readily available, instead, the correlations are tested in a hierarchal fashion. First, we test whether all correlations are zero; if that null hypothesis is rejected, we can test whether the $I-1$ (I is the total number of canonical correlations) smallest correlations are—or whether the remaining shared variance is—zero, etc.

4.1 Full conditionals of the latent variables and the covariance

4.1.1 Multiplicative UV effect

The item responses are related to the network through the dependence between $\boldsymbol {\Theta }$ and $\boldsymbol {U}, \boldsymbol {V}$ . We are interested in the joint full conditional distribution of $\boldsymbol {\Theta }$ and $\boldsymbol {U}, \boldsymbol {V}$ . For person p, we focus on the first dimension for sender, $u_{p,1}$ conditional on the other dimensions, $u_{p,2},\ldots ,u_{p,K}$ and all dimensions of $\boldsymbol {v}_p $ , $\boldsymbol {v}_p = (v_{p,1},\ldots ,v_{p,K})^T$ . Given that $\boldsymbol {u}_p, \boldsymbol {v}_p$ , and $\boldsymbol {\theta }_p$ follow a multivariate normal distribution, the joint probability model for $u_{p,1}$ and $\theta _{p,1}$ ,…, $\theta _{p,D}$ conditional on $u_{p,2},\ldots ,u_{p,K}$ and $\boldsymbol {v}_p$ is also a multivariate normal distribution with conditional mean $\boldsymbol {\mu }_{u\theta |\dots }$ and conditional covariance matrix $\boldsymbol {\Sigma }_{u\theta |\dots }$ . We can define the conditional covariance matrix as a block matrix: $ \boldsymbol {\Sigma }_{u\theta |\dots } = \begin {pmatrix} \Lambda _u &\Lambda _{u\theta }^T\\ \Lambda _{u\theta } &\Lambda _{\theta } \end {pmatrix}, \nonumber $ and the inverse of this block matrix is

(4.1) $$ \begin{align} \boldsymbol{\Sigma}_{u\theta|\dots}^{-1} = \begin{pmatrix} \Lambda_u &\Lambda_{u\theta}^T\\ \Lambda_{u\theta} &\Lambda_{\theta} \end{pmatrix}^{-1} = \begin{pmatrix} (\Lambda_u - \Lambda_{u\theta}^T \Lambda_{\theta}^{-1} \Lambda_{u\theta})^{-1} & - (\Lambda_u - \Lambda_{u\theta}^T \Lambda_{\theta}^{-1} \Lambda_{u\theta})^{-1} \Lambda_{u\theta}^T \Lambda_{\theta}^{-1}\\ - (\Lambda_{\theta} - \Lambda_{u\theta} \Lambda_u^{-1} \Lambda_{u\theta}^T)^{-1} \Lambda_{u\theta} \Lambda_u^{-1} &(\Lambda_{\theta} - \Lambda_{u\theta} \Lambda_u^{-1} \Lambda_{u\theta}^T)^{-1} \end{pmatrix}. \end{align} $$

We can further define the inverse of the conditional covariance matrix as another block matrix: $ \boldsymbol {\Sigma }_{u\theta |\dots }^{-1} = \begin {pmatrix} Q_u &Q_{\theta u}\\ Q_{u\theta } & Q_{\theta }\end {pmatrix}, $ with each component as a function of $\Lambda $ s. Therefore, the probability model for $u_{p,i}, \theta _{p,1},\ldots ,\theta _{p,D}$ conditional on $u_{p,2},\ldots , u_{p,K}, v_{p,1},\ldots ,v_{p,K}$ can be written as

(4.2)

where $S_u$ is the first block of the block matrix

Let us now look at the first dimension for all persons and rewrite the network component as: $\boldsymbol {R} = \boldsymbol {X}- \delta \boldsymbol {1} \boldsymbol {1}^T - \sum _{k=2}^K \boldsymbol {u}_k \boldsymbol {v}_k^T $ , where $\boldsymbol {u}_k$ is the $N \times 1$ vector representing the kth network dimension for sender, and $\boldsymbol {v}_k$ is the $N \times 1$ vector representing the kth network dimension for receiver. Then, we have $\boldsymbol {R} = \boldsymbol {u}_1^T \boldsymbol {v}_1 + \boldsymbol {E}$ . Decorrelating the error, we have $\boldsymbol {\tilde {R}} = c \boldsymbol {R} + d \boldsymbol {R}^T$ , where $ c= \sigma _e^{-1} ((1+\rho )^{-1/2} + (1- \rho )^{-1/2})/2$ and $ d= \sigma _e^{-1} ((1+\rho )^{-1/2} - (1- \rho )^{-1/2})/2$ . We can write the model for $\boldsymbol {\tilde {R}}$ in a vectorized form and obtain the likelihood for the decorrelated network as $ f(\boldsymbol {\tilde {r}} | \boldsymbol {v}_1, \boldsymbol {u}_1) \propto \exp \left ( - \frac {1}{2} \left ( \boldsymbol {\tilde {r}} - \boldsymbol {M} \boldsymbol {u}_i \right )^T\left ( \boldsymbol {\tilde {r}} - \boldsymbol {M} \boldsymbol {u}_i \right ) \right )$ and $\boldsymbol {M} = c(\boldsymbol {v}_1 \otimes \boldsymbol {I}) + d(\boldsymbol {I} \otimes \boldsymbol {v}_1)$ .

We can write the full conditional distribution of $\boldsymbol {u}_{1}$ :

(4.3)

The full conditional distribution of the other network dimensions and the latent variables of item responses can be derived in a similar fashion.

Recall that prior for $\boldsymbol {\Sigma }_{u \theta }$ is $\boldsymbol {\Sigma }_{u \theta }^{-1} \sim \text {Wishart} (\boldsymbol {I}_{2K+D}, 2K+D+2)$ . Let $\boldsymbol {F'}$ be an $N \times (2K+D)$ matrix with the pth row as $(\boldsymbol {u}_{p}^T, \boldsymbol {v}_{p}^T, \boldsymbol {\theta }_{p}^T)$ . The full conditional for $\boldsymbol {\Sigma }_{u \theta }$ follows a inverse-Wishart $(\boldsymbol {I}_{2K+D} + \boldsymbol {F'}^T \boldsymbol {F'}, N+2K+D+2)$ .

4.2 Full conditionals of $\sigma ^{-2}_e$ , $\rho $ , and $\delta $

To derive the full conditional distribution of the network error term, we look at the network model with only the error term: $\boldsymbol {E} = \boldsymbol {X} - \delta \boldsymbol {1} \boldsymbol {1}^T - \boldsymbol {U}^T \boldsymbol {U}$ for the bilinear effect, and $\boldsymbol {E} = \boldsymbol {X} - \delta \boldsymbol {1} \boldsymbol {1}^T - \boldsymbol {U}^T \boldsymbol {V}$ for the multiplicative UV effect. Recall that the prior for $\sigma _e^{-2}$ is gamma $(1/2, 1/2)$ . Therefore, the full conditional of $\sigma _e^{-2}$ is also gamma $\left (\frac {N^2 + 1}{2}, 1/2 \left ( 1+ \sum _{a < b} \begin {pmatrix} e_{a,b}\\ e_{b,a} \end {pmatrix}^T \begin {pmatrix} 1 & \rho \\ \rho & 1 \end {pmatrix}^{-1} \begin {pmatrix} e_{a,b}\\ e_{b,a} \end {pmatrix} + \sum _{a =1}^N (1 + \rho )^{-1} e_{a,a} ^2 \right )\right )$ .

For $\rho $ , the full conditional distribution of the within-dyad correlation is not a well-known distribution. To update parameter $\rho $ , we use Metropolis–Hastings with a uniform prior between $-1$ and $1$ . We approximate the full conditional distribution by sampling values from a proposed probability model and accept with some probability. The estimation of the intercept parameter, $\delta $ , can be seen as a Bayesian linear regression problem with a design matrix $\boldsymbol {1} \boldsymbol {1}^T $ and a normal prior.

4.3 Full conditionals of item parameters and $\sigma ^{-2}_{\epsilon }$

Recall that $\boldsymbol {\xi }_i$ is the $(D +1) \times 1$ vector of parameters for item i, $\boldsymbol {\xi }_i = (\boldsymbol {\alpha }_i^T, \beta _i)^T$ . To derive the full conditional distributions of the item parameters, we rewrite the model for the ith item as

(4.4) $$ \begin{align} \boldsymbol{y}_i = \boldsymbol{G} \boldsymbol{\xi}_i + \boldsymbol{\epsilon}_i, \end{align} $$

where $\boldsymbol {y}_i$ is the ith column vector of the item response matrix; $\boldsymbol {G}$ is an $N \times (D + 1)$ design matrix, $\boldsymbol {G} = (\boldsymbol {\Theta }, \boldsymbol {1}_{N \times 1})$ ; and $\boldsymbol {\Theta }$ is the $N \times D$ matrix of the latent variables of the item responses. The error of the item response model follows a normal distribution, $\epsilon _{p,i} \overset {iid}{\sim } N(0, \sigma ^2_{\epsilon })$ .

Recall that the prior for $\boldsymbol {\xi }_i$ is a multivariate normal distribution, $\boldsymbol {\xi }_i \sim N (\boldsymbol {\mu }_{\xi }, \boldsymbol {\Sigma }_{\xi })$ , then the full conditional of $\boldsymbol {\xi }_i$ also follows a multivariate normal distribution with mean $\boldsymbol {\mu }_{\xi }^{\ast}$ and variance $\boldsymbol {\Sigma }_{\xi }^{\ast}$ : $\boldsymbol {\mu }_{\xi }^{\ast} = \Sigma _{\xi }^{\ast} \left ( \sigma ^{-2}_{\epsilon } \boldsymbol {G}^T \boldsymbol {\eta }_i + \boldsymbol {\Sigma }_{\xi }^{-1} \boldsymbol {\mu }_{\xi } \right )$ , $ \Sigma _{\xi }^{\ast} =\left ( \sigma ^{-2}_{\epsilon } ( \boldsymbol {G}^T \boldsymbol {G}) + \boldsymbol {\Sigma }_{\xi }^{-1} \right )^{-1}$ .

The prior for $\sigma ^{-2}_{\epsilon }$ is gamma (1/2, 1/2). Therefore, the posterior for $\sigma ^{-2}_{\epsilon }$ is also gamma $( \frac {N \ast M +1}{2}, 1/2 \ast (1 + \sum _{p=1}^N \sum _{i=1}^M \epsilon _{p,i}^2 ) )$ .

For binary data, $X_{a,b}$ and $y_{p,i}$ are functions of the latent responses, $\phi _{a,b}$ and $\eta _{p,i}$ . The data can be seen as binary indicators of the latent responses such that $X_{a,b} = 1$ if $\phi _{a,b}> 0$ and $Y_{p,i} = 1$ if $\eta _{p,i}> 0$ . The error variances of the latent responses, $\sigma ^2_e$ and $\sigma ^2_{\epsilon }$ , are assumed to be $1$ .

5.1 Estimation

Because the structures of the advice-seeking networks are distinct across different schools, and there is not much inter-school activity, we fit the JNIRM to each school separately. To be succinct in our report, we chose one school, school 56, as a more detailed demonstration, and summarized the results of the other schools. See discussions about the different network structures recovered for different schools and detailed reports for applications with multiple schools and other individual schools in the Supplementary Material. There were a total of $26$ teachers in school 56, and the density of the directed network is $0.143$ . The receiver and the sender degrees had a negative correlation of $-0.514$ , suggesting that in this school, teachers who often gave advice tended not to seek advice, and vice versa. Note that this finding is not replicated in every school, an example of the difference in network structures in different schools. The MCMC algorithm described in Section 4 was used to obtain the posterior distributions. Each model was fitted with $200{,}000$ iterations with a burn-in period of $10{,}000$ and thinning every 10th sample. Each model took about 31 minutes to complete with 97.6 MB memory on the Apple M2 chip with 8 GB RM Mac OS. When extended to a large network with approximately 450 nodes, the model takes took about 12 hours to complete with 103 MB memory on the Apple M2 chip with 8 GB RM Mac OS. We used the trace plots along with convergence diagnostics as the convergence criteria, and neither showed obvious signs of non-convergence (see details in the Supplementary Material).

5.2 Dimensionality of the network

We present two methods for choosing the number of latent dimensions in the network (see further investigations of dimensionality in the Supplementary Material). For the first method, we compared the relative proportion of variation explained by each dimension following Fosdick and Hoff (Reference Fosdick and Hoff2015). This approach is similar to the scree plot method commonly seen in PCA except that each dimension is estimated based on the proposed joint framework instead of the PCA. Recall that the number of dimensions in the item responses is chosen as 3 based on the number of theoretical constructs, and therefore does not need a data-driven solution. Figure 4a presents the proportion of variation in $\boldsymbol {\hat {U}} \boldsymbol {\hat {V}}^T$ explained by each dimension of the JNIRM with $K=9$ and $D=3$ fitted to the advice-seeking network and the school climate questionnaire. The results show that there are likely four dimensions for the network component of the model given the big drop in the proportion of variation explained after four latent components. For the second method, we evaluated the out-of-sample model fit under different numbers of dimensions, $K \in {2,3,4,5,6,7}$ . We randomly selected one row of the social network (seeking advice) as the test data and the other rows as the training data to obtain the out-of-sample predictive fit, and we repeated the process for each row of the network. The model uses the node’s receiving behaviors and the item responses to perform the out-of-sample prediction for each node’s seeking behaviors. Figure 4b shows the out-of-sample AUC values on the test data when the number of dimensions ranges from 1 to 7. The results show that the fit of the JNIRM with $K=4$ was the most optimal with the highest AUC values. Based on the convergent results of both methods, we believe that four is the best choice for the number of network dimensions.

Figure 4

(a) The proportion of variation explained by each dimension for school 56. (b) The out-of-sample AUC values on the test data when the number of dimensions is from 1 to 7.

Figure 4 Long description

On the left, a vertical bar graph plots proportion of variation on the y-axis against dimension on the x-axis. The first bar is highest, near 0.5, followed by decreasing bars for dimensions two through seven, each bar lower than the previous. On the right, a boxplot displays A U C on the y-axis versus dimension on the x-axis, with seven boxplots labeled one to seven. Each boxplot shows median, interquartile range, and whiskers. Median A U C values vary, with dimension four showing the highest median, while others fluctuate between approximately 0.6 and 0.8. Whiskers extend from about 0.4 to 1.0 for all dimensions.

5.3 Model fit

The joint model was estimated with the network component defined in Equation (3.1) using $K=4$ , the item response component defined in Equation (3.2) using $D=3,$ and the joint component defined in Equation (3.3). See extensive investigations of the school-level model fit results for item responses as well as networks in the Supplementary Material. As mentioned before, the random node-specific intercepts were not included in the model given that the recoveries of the node degrees were comparable with and without random intercepts in the model (see the Supplementary Material for detailed discussion on the role of AME in network latent space model), and no substantial improvement in model fit was observed when random intercepts were included. The network dimensions for sender and receiver, $\boldsymbol {U}$ and $\boldsymbol {V}$ , were estimated by the first four left and right singular vectors of the posterior mean of the vector product, $\boldsymbol {U} \boldsymbol {V}^T$ . The latent variables for the item responses and the item slopes, $\boldsymbol {\Theta }$ and $\boldsymbol {A}$ , were estimated by the first three left and right singular vectors of the posterior mean of the vector product, $\boldsymbol {\Theta } \boldsymbol {A}^T$ . We performed a target rotation on the estimated item slopes using the same procedure as described before. The rotated slopes again show clear correspondence with the theoretical structure of the dimensions of satisfaction, the perceived influence over school policies, and the perception of students. See details of the rotation and information about other parameter estimates in the Supplementary Material.

For the advice-seeking network, the in-sample AUC and the median out-of-sample AUC were estimated as $0.998$ and $0.800$ , showing a reasonable fit. In addition, we assessed the fit of the joint model to the data using posterior predictive checking. More specifically, we assessed the posterior predictive fit using $200{,}000$ replicated data sets based on the estimated model parameters from the MCMC chain. If the estimated statistics based on the replicated data recover the observed statistics in the observed data, it shows that the model fits the data reasonably well. To investigate the fit of the joint model, we assess the recovery of the node degrees, mean item responses per item and per response category, and the item covariances through posterior predictive checking, which all show satisfactory fit of the model (details are in the Supplementary Material).

5.4 Interpretation of the network latent space

We first looked at the relationships between the estimated network dimensions and the observed node degrees. Table 3 presents the correlations between the estimated network dimensions and the observed sender and receiver degrees. Figure 5a displays the estimated latent positions in the latent subspace based on the first two dimensions. The teachers’ IDs are shown as the numbers in the plots. The physical size of the ID reflects the node degrees. For example, teacher 92 was the most active advice giver, and teacher 18 was the most active advice seeker. The colors differentiate between sender (black) and receiver (red). We focus on first two dimensions as our subsequent hypothesis tests for the association between advice-seeking networks and school climate dimensions show that only the first canonical correlation is significant, and that the first canonical correlation is only associated with first two network dimensions based on the standardized canonical function coefficients. In Table 3 and Figure 5, we can see that the sender and receiver coordinates on the first dimension were highly correlated with the node degrees: $0.685$ (sender degree) and $-0.923$ (receiver degree). The first dimension also differentiated senders from receivers with receivers at the positive side of the x axis and senders at the negative side of the x axis. In this way, the first dimensions not only captured the sender and receiver degrees, but also the negative correlations between the sender and receiver node degrees. In this school, teachers who often gave advice tended not to seek advice, and this phenomenon was captured by the first dimension. In Table 3, the sender coordinates on the second dimension showed moderate negative correlations with the sender degrees, unlike those on the first dimension, which seemed to be unrelated to the receiver degrees. The sender coordinates on the third dimension showed moderate negative correlations with the sender degrees, which suggests that teachers positioned high on the third sender dimension have low sender degrees and tend not to seek advice. Different from the first sender dimension, the third sender dimension is unrelated to the receiver degrees. The receiver coordinates on the third dimension also showed moderate negative correlations with the receiver degrees, which suggests that teachers positioned high on the third receiver dimension have low receiver degrees and tend not to give advice.

Table 3

Correlations between network latent dimensions and node degrees

Table 3 Long description

The table has two main sections: Sender and Receiver latent dimensions. For Sender, D equals 1 has Receiver correlation negative 0.413 and Sender correlation 0.685. D equals 2 has Receiver 0.079 and Sender negative 0.424. D equals 3 has Receiver 0.125 and Sender negative 0.521. D equals 4 has Receiver 0.071 and Sender negative 0.035. For Receiver, D equals 1 has Receiver correlation negative 0.923 and Sender 0.584. D equals 2 has Receiver negative 0.249 and Sender 0.009. D equals 3 has Receiver negative 0.375 and Sender 0.016. D equals 4 has Receiver 0.143 and Sender 0.087. Negative correlations are strongest for Receiver at D equals 1.

Figure 5

(a) The estimated latent positions for the first and second dimensions of the latent space. The teachers’ IDs are shown as numbers in the plots. The physical size of the ID reflects the node degree. The colors differentiate between sender (black) and receiver (red). (b) The same plot, except that the colors differentiate whether the nodes identify as women, and size of the numbers is constant.

Figure 5 Long description

Panel (a) shows a scatter plot with Dimension 1 on the x-axis and Dimension 2 on the y-axis. Each point is labeled with a teacher ID number. Node size reflects degree, with larger numbers indicating higher degree. Black labels represent senders, red labels represent receivers. IDs such as 92 and 05 are large and red, indicating high-degree receivers, while 18 is large and black, indicating a high-degree sender. Panel (b) uses the same axes and ID labels, but all numbers are uniform in size. Color coding distinguishes gender: green for female, black for non-female. IDs such as 32 and 16 are black, while most others are green. Both panels display the spatial distribution of teachers in latent space, with clustering and separation visible along both axes.

In addition to the relationships with node degrees, the latent space can also reflect network homophily, which means that the relationships of teachers with each other depend on attributes, such as gender, educational background, personality, cultural heritage, etc. In Figure 5b, we present the first and the second dimensions of the latent space colored by whether the teachers identified as female. It seems that those who did not identify as female were positioned higher on the second dimension of the latent space than the female teachers. Therefore, gender could potentially serve as an example from a myriad of possibilities why teachers sought advice from some teachers but not from others.

5.5 Relationship between network and item responses

The test of independence rejects of the null hypothesis of independence between the network and item responses with Wilks’ $\lambda $ of $0.084$ and p-values of $0.009$ . Subsequently, we performed the sequential hypothesis tests for the canonical correlation coefficients. There are a total of three pairs of canonical functions. We performed two sequential tests assessing (1) the null hypothesis that the remaining relation after removing the first pair of canonical functions is zero and (2) the null hypothesis that the remaining relation after removing the first and second pair of canonical functions is zero. The first test resulted in a Wilks’ $\lambda $ of $0.299$ and a p-value of $0.124$ , and the second test resulted in a Wilks’s $\lambda $ of $0.656$ and a p-value of $0.312$ . Therefore, we failed to reject both null hypotheses, and we focused on interpreting the first canonical correlation.

Table 4 presents the CCA results associated with the first canonical correlation. In the table, standardized canonical function coefficients are presented. For emphasis, coefficients for selected latent variables with large coefficients (in absolute values) were underlined. Regarding the item responses, the third dimension of the item responses was the primary contributor to the first canonical correlation. The third dimension of the item responses had a large negative weight in the first canonical function. Recall that the third dimension was the satisfaction dimension. For the network, there were three large weights for the first canonical function: Sender 1 had a large negative weight; Sender 2 and Receiver 1 had large positive weights. Therefore, the satisfaction dimension was positively related to Sender 1, and negatively related to Sender 2 and Receiver 1. Sender 1 was highly positively correlated with sender degree ( $0.685$ ), and it had a high negative weight in the canonical function ( $-0.912$ ). Satisfaction had a high negative weight as well ( $-0.889$ ). We can thus conclude that there was a positive relationship between being satisfied with the school and the frequency of seeking advice. Receiver 1 was highly negatively correlated with receiver degree ( $-0.923$ ), and it had a high positive weight in the canonical function ( $1.007$ ). We can also conclude that there was a positive relationship between being asked for advice and being satisfied with the school. Sender 2 showed similar relationships. Sender 2 was negatively correlated with sender degree ( $-0.424$ ), and it had a high positive weight in the canonical function. Both asking for advice and being asked for advice seemed to be positively associated with being satisfied with the school.

Table 4

Canonical correlation results for the first canonical functions

Table 4 Long description

From top to bottom, the table lists latent variables and their standardized function coefficients. Network Sender 1 has negative zero point nine one two, Network Sender 2 has zero point nine eight four, Network Sender 3 has zero point zero two one, Network Sender 4 has zero point zero four zero, Network Receiver 1 has one point zero zero seven, Network Receiver 2 has negative zero point two seven seven, Network Receiver 3 has negative zero point three two six, Network Receiver 4 has zero point two six two, Student has zero point one five zero, Policy has zero point one zero zero, and Satisfaction has negative zero point eight eight nine. Underlined values are Network Sender 1, Network Sender 2, Network Receiver 1, and Satisfaction. The largest positive coefficient is for Network Receiver 1, and the largest negative coefficient is for Network Sender 1.

In Table 4, Sender 1 and Receiver 1 had function coefficients with opposite signs—senders high on the first dimension of the network were more likely to have positive reports of satisfaction (high on the satisfaction dimension); and receivers high on the first dimension of the network were more likely to have less positive reports of satisfaction (low on the satisfaction dimension). Thus, in School 56, the complementarity principle applies, and teachers get in touch with those who score in an opposite way on the satisfaction dimension. The complementarity principle can also be observed in School 24, where teachers get in touch with those who score in an opposite way on the student dimension (see the Supplementary Material).

5.6 Comparison to separate network and item response models

The rejection of the null hypothesis of independence between the network and item responses for school 56 suggested that the item responses were relevant information for the network, and vice versa. With the incorporation of relevant new information, we expect an improvement in model fit to data using a joint modeling framework over modeling the data sets separately given that reasonable models are specified on both sides and that there is dependence. In this section, we assess whether there is improvement in model fit using the joint framework compared with the separate models for network and item responses using accuracy of out of sample prediction. We fitted the advice-seeking network to the same network model in Equation (3.3) with $K=4$ and no item responses using the amen package (Hoff, Reference Hoff2015). Figure 6a shows the out-of-sample AUC values using the separate AME model versus JNIRM. The median out-of-sample AUC value was $0.800$ with JNIRM versus $0.727$ with AME suggesting an improvement in model fit with the joint modeling framework. In Figure 7, we show the out-of-sample AUC values for predicting network edges using our method versus AME across all schools. The results show that our approach has comparable or better model performance in most schools. In Figure 6, we also compare the recovery of network statistics, such as node degrees, dyadic dependence, transitivity and balance based on the replicated data sets. The replicated data sets were obtained for posterior predictive checking in the same way as before. For the recovery of the node degrees, the differences were smaller based on both the sender and the receiver degrees using JNIRM versus AME (see Figures 6c and 6d). For the recovery of dyadic dependence and balance, the distributions based on JNIRM were narrower than the distributions based on AME suggesting that there was more precision using JNIRM than AME.

Figure 6

(a) The out-of-sample AUC values using the separate AME model versus the JNIRM. (b) The density of three network statistics based on the replicated data using AME versus JNIRM. The vertical lines represent the network statistics in the observed data. The box plots of the absolute differences between the predicted node degrees and the observed node degrees based on AME versus JNIRM. (c) The box plots of the absolute differences between the predicted sender degrees and the observed sender degrees based on AME versus JNIRM. (d) The box plots of the absolute differences between the predicted sender degrees and the observed sender degrees based on AME versus JNIRM.

Figure 6 Long description

From left to right, panel one shows box plots of A U C values for AME and J N I R M, with AME slightly lower median. Panel two contains three density plots for dyadic dependence, balance, and transitivity, each comparing J N I R M (red) and AME (blue) against observed statistics marked by vertical lines. Panel three presents box plots of absolute differences in sender degrees, with AME showing higher median and spread than J N I R M. Panel four displays box plots of absolute differences in receiver degrees, again with AME having higher median and variability compared to J N I R M. All axes and labels are precisely transcribed as shown.

Figure 7

The leave-one-out AUC values using the separate AME model versus the JNIRM across all schools.

Figure 7 Long description

The horizontal axis lists School I D values in the order 7, 63, 64, 44, 27, 32, 15, 24, 56, 58, 57, 35, 8, 65. For each School I D, two adjacent vertical boxplots are shown: blue for A M E and magenta for J N I R M. The vertical axis is labeled A U C and ranges from 0.1 to 1.0 in increments of 0.2. Most boxplots for both models are centered between 0.6 and 0.9 A U C, with some outliers above and below. The spread and median of A U C values vary by School I D, with some schools showing higher median A U C for A M E, others for J N I R M. Outliers are marked as black dots. The legend at the bottom identifies blue as A M E and magenta as J N I R M.

To compare the joint framework with the separate model for the item responses, we also fitted the school climate item responses to the same item response model in Equation (3.3) with $D = 3$ and no advice-seeking network. We compared the average predicted item responses across replicated data per observed category and per item in addition to the recovery of the item covariances. We found that there was no noticeable difference between the joint and the separate model in the average predicted item responses (see the Supplementary Material for details). On the other hand, the joint framework was found to have better recovery of the item covariances with a correlation of $0.642$ between the observed and the estimated item covariances versus $0.488$ in the separate model. The results for the network data and the questionnaire data show that the joint modeling framework has the potential to improve the model fit and the predictive fit of the data over separate models.

5.7 Summary of the relationships

Out of the 14 schools, Schools 24, 56, and 63 showed significant relationships (with the significance level of $0.05$ ) between the network and item responses with Wilks’ $\lambda $ of $0.186$ , $0.084$ , and $0.120$ and p-values of $0.004$ , $0.009$ , and $0.005$ . For all $14$ schools, we summarize the relationship between the network and item responses based on the first canonical correlations given that the first canonical correlations were estimated to be consistently large. We focus on interpreting the dimensions of the item responses and their relationships with the advice-seeking networks.

For the dimensions of the item responses, the student dimension was found to be less clearly replicated than the policy and the satisfaction dimensions with the median congruence coefficients (between the rotated dimensions and the theoretical structure) being $0.565$ , $0.885$ , and $0.790$ for the student, policy, and satisfaction dimensions, respectively. To assess which dimension(s) contribute(s) to the first canonical correlations, we compared the size of the standardized function coefficients for each dimension across schools. Table 5 shows the standardized function coefficients for all schools. Keep in mind that there is subjectivity associated with interpreting CCA results—a standardized function coefficient may seem large to one person and not large enough to the next. The results show that in $9$ out of the $14$ schools, the student dimension had the largest function coefficients; in only $1$ out of the $14$ schools, the policy dimension had the largest function coefficient; and in $8$ out of the $14$ schools, the satisfaction dimension had the largest or the second largest coefficients. Overall, the CCA results show that the perception of students and the satisfaction are related to the advice-seeking relationships in most schools, but the same cannot be said for the perceived influence over school policies.

Table 5

Dependence between the network and item responses

Table 5 Long description

The table lists School IDs in the first column, followed by three columns labeled Student, Policy, and Satisfaction, each containing standardized function coefficients. From top to bottom: School ID 15 has Student 2.231, Policy negative 0.268, Satisfaction negative 1.489. School ID 24 has Student negative 0.874, Policy negative 0.139, Satisfaction negative 0.226. School ID 27 has Student negative 1.206, Policy 0.523, Satisfaction 0.320. School ID 32 has Student negative 1.191, Policy 0.275, Satisfaction 0.681. School ID 35 has Student 1.377, Policy negative 0.753, Satisfaction 0.292. School ID 44 has Student negative 0.404, Policy 0.643, Satisfaction 0.812. School ID 56 has Student 0.241, Policy 0.546, Satisfaction 1.437. School ID 57 has Student 0.851, Policy negative 0.317, Satisfaction 0.489. School ID 58 has Student 0.824, Policy 0.434, Satisfaction 2.001. School ID 63 has Student 0.807, Policy 0.391, Satisfaction negative 1.475. School ID 64 has Student 7.979, Policy negative 1.364, Satisfaction negative 7.266. School ID 65 has Student 0.601, Policy 0.166, Satisfaction 0.581. School ID 7 has Student 6.530, Policy 2.332, Satisfaction negative 4.326. School ID 8 has Student negative 0.390, Policy 0.892, Satisfaction negative 0.473. Negative values are indicated by the word negative before the number.

6.1 Bias in sparse networks with finite samples

The density of the binary network is influenced by both the intercept and the variances of the network dimensions. We conducted a small simulation study to assess whether there is bias in the estimation of the intercept and the variances of the network dimensions.

We first investigated how the density of the binary network depends on the intercept and the variances of the network dimensions. To do that, we generated data sets under the proposed network model in Equation (3.1) with different intercepts and different variances of the network dimensions (with no within-dyad correlation), one data set per combination of an intercept value with a variance value; and we compared the densities of the resulting binary networks. Table 7 presents the densities of the binary networks when the intercepts are $0$ , $-1$ , $-2$ , $-3$ , and $-4$ ; and the variances of the network dimensions, U and V, are $0.2$ versus $1$ . For simplicity, we assumed the same variance for all dimensions of U and V and orthogonality among the dimensions; when the variance was $1$ , it means that an identity matrix was used as the covariance for the network dimensions. In Table 7, both the intercept and the variances of the network dimensions influenced the density of the network. Therefore, a biased estimate for the intercept can potentially cooccur with a biased estimate of the latent variable variance while preserving the density of the network, for example, the intercept can be overestimated while the latent variable variance is underestimated.

Table 7

Density of binary networks

Table 7 Long description

The table has three columns and six rows. The top row contains the header ‘Variance of the network dimensions’ spanning two columns. The second row lists the intercept in the first column, then variance values zero point two and one as column headers. The first column lists intercept values in order from top to bottom as zero, negative one, negative two, negative three, and negative four. For variance zero point two, densities are zero point four nine eight, zero point one six five, zero point zero two five, zero point zero zero one, and zero. For variance one, densities are zero point five, zero point two four seven, zero point zero nine six, zero point zero three three, and zero point zero one two. Densities decrease as the intercept becomes more negative, and are consistently higher for variance one than for zero point two.

Note: The table contains the densities of the generated binary networks in vector latent space models with only the intercept and the vector product. The table contains the densities of the binary networks when the intercepts are $0$ , $-1$ , $-2$ , $-3$ to $-4$ ; and the variances for the network dimensions, U and V, are $0.2$ versus $1$ .

Second, we investigated whether there is bias in the estimation of the intercept and the variances of the network dimensions. To do that, we fitted the generated data sets with $N=100$ and 1,000 to the proposed network model and calculated the bias of the intercepts and the variances of the network dimensions. We used the five data sets generated in conditions specified in the second column of Table 7 with the two-dimensional multiplicative UV effects that have an identity covariance matrix and decreasing intercepts $(0, -1, -2, -3, \text { and} -4)$ . We fitted these data sets with $N=100$ to the network model with an intercept and a two-dimensional multiplicative effect UV effect shown in Equation (3.1) and repeated the process for $N=1{,}000$ . The biases of the intercept and the biases of the variances of the network dimensions are presented in Table 8. In finite samples, as the intercept decreased, the estimated intercept became more biased in the positive direction. For $N =100$ , the intercepts showed an overestimation of $0.010$ , $0.017$ , $0.018$ , $0.183$ , and $0.835$ when the true intercepts were $0, -1, -2, -3, \text {and} -4,$ respectively. Further, for $N=100$ , the variances showed an underestimation problem, and the estimates became more biased as the networks became sparser (the intercept became more negative). However, as we increase the sample size to $N=1,000$ , the biases in both the estimates of the intercept and the estimates of the variance of multiplicative effects decrease substantially. Overall, we note that the biases decrease as we increase the sample size N and the density of the network, which is controlled by the intercept term. The excess Kurtosis values of the latent variables are also presented in Table 8. Note that excess kurtosis is calculated by subtracting 3 from the raw Kurtosis. Since the Kurtosis of a normal distribution is 3, values greater than 3 indicates heavier tail distributions. The results show that when $N=100$ , latent variable distributions became more heavy-tailed than the normal distribution when the networks were sparse. When $N=1{,}000$ , the excess Kurtosis was not substantial and only noticeable when the true intercept was $-4$ .

Table 8

Parameter recovery

Table 8 Long description

The table is organized in two main sections by sample size, N equals 100 and N equals 1,000. Each section contains rows for true intercept values 0, negative 1, negative 2, negative 3, and negative 4. Columns are grouped under Bias and Excess kurtosis. Bias columns include Intercept, Var U D1, Var U D2, Var V D1, and Var V D2. Excess kurtosis columns include U D1, U D2, V D1, and V D2. For N equals 100, at true intercept 0, bias values are 0.010 for Intercept, negative 0.033 for Var U D1, negative 0.134 for Var U D2, negative 0.021 for Var V D1, negative 0.136 for Var V D2. Excess kurtosis values are negative 0.490 for U D1, negative 0.610 for U D2, negative 0.345 for V D1, negative 0.259 for V D2. As the true intercept decreases, bias and excess kurtosis values generally become more negative, with the largest deviations at negative 4. For N equals 1,000, bias and excess kurtosis values are closer to zero across all intercepts, indicating improved parameter recovery with larger sample size. For example, at true intercept 0, bias values are negative 0.002 for Intercept, 0.011 for Var U D1, negative 0.076 for Var U D2, 0.011 for Var V D1, negative 0.076 for Var V D2. Excess kurtosis values are 0.001 for U D1, negative 0.011 for U D2, negative 0.291 for V D1, 0.077 for V D2. The note clarifies that excess kurtosis is reported as raw kurtosis minus 3, and that positive bias values indicate overestimation while negative values indicate underestimation.

Note: Kurtosis entries report excess Kurtosis (raw kurtosis minus 3). Var(U) is estimated based on the covariance of the estimated U. U is estimated as the K left singular vectors of the posterior mean of the vector products. The same is true for Var(V) and V. For bias entries, positive values indicate overestimation, and negative values indicate underestimation. The first column gives the true intercept.

The results show that in sparse networks with finite samples, there is positive bias in the estimation of the intercept and negative biases in the estimation of the variances of the network dimensions. This is an issue to consider when the networks are extremely sparse with a density around $0.01$ or less in finite samples and when we need to make separate inferences for the intercept and the variances of the network dimensions. The issue disappears when we make an inference that jointly considers the intercept and the variances of the network dimensions, for example, estimating the density of the network. If the network is not sparse, there is (almost) no bias regardless of the sample size; and when the sample size is sufficiently large, the intercepts and the variances of the network dimensions can be correctly recovered. In our applications, the densities of the networks were always larger than we $0.01$ . Besides, we focus on making inferences about the relationship between the network and the item responses, and the separate inferences about the variances of the network dimensions and the intercept are not the focus of this project.