| Models Info | ||
|---|---|---|
| Estimation Method | ML | . |
| Number of observations | 12 | |
| Free parameters | 13 | |
| Converged | TRUE | |
| Loglikelihood user model | -6.819 | |
| Loglikelihood unrestricted model | -6.819 | |
| Model | Loyalty ~ Satisfaction + Type + Service + Food + Income | |
| Satisfaction ~ Food + Service + Income + Type | ||
| Model Tests | |||
|---|---|---|---|
| Label | X² | df | p |
| Baseline Model | 67.42 | 9 | <.001 |
| Fit Indices | |||||||
|---|---|---|---|---|---|---|---|
| RMSEA 95% CI | |||||||
| AIC | BIC | adj. BIC | SRMR | RMSEA | Lower | Upper | RMSEA p |
| 39.64 | 45.94 | 6.63 | 0.00 | 0.00 | 0.00 | 0.00 | NaN |
| Fit Indices | |||||
|---|---|---|---|---|---|
| CFI | TLI | RNI | GFI | adj. GFI | pars. GFI |
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 |
| R-squared | |||
|---|---|---|---|
| 95% Confidence Intervals | |||
| Variable | R² | Lower | Upper |
| Loyalty | 0.97 | 0.90 | 0.99 |
| Satisfaction | 0.87 | 0.58 | 0.96 |
| Parameter Estimates | ||||||||
|---|---|---|---|---|---|---|---|---|
| 95% Confidence Intervals | ||||||||
| Dep | Pred | Estimate | SE | Lower | Upper | β | z | p |
| Loyalty | Satisfaction | 0.52 | 0.13 | 0.27 | 0.78 | 0.51 | 3.98 | <.001 |
| Loyalty | Type1 | -0.34 | 0.35 | -1.03 | 0.36 | -0.13 | -0.96 | .339 |
| Loyalty | Service | 0.05 | 0.10 | -0.13 | 0.24 | 0.06 | 0.56 | .575 |
| Loyalty | Food | 0.11 | 0.09 | -0.07 | 0.29 | 0.12 | 1.23 | .219 |
| Loyalty | Income | 0.04 | 0.02 | 0.00 | 0.07 | 0.45 | 2.23 | .026 |
| Satisfaction | Food | -0.06 | 0.20 | -0.45 | 0.33 | -0.06 | -0.29 | .769 |
| Satisfaction | Service | 0.27 | 0.19 | -0.11 | 0.65 | 0.32 | 1.41 | .159 |
| Satisfaction | Income | 0.03 | 0.04 | -0.04 | 0.11 | 0.37 | 0.83 | .405 |
| Satisfaction | Type1 | 1.01 | 0.72 | -0.41 | 2.43 | 0.39 | 1.39 | .164 |
| Variances and Covariances | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 95% Confidence Intervals | ||||||||||
| Variable 1 | Variable 2 | Estimate | SE | Lower | Upper | β | z | p | Method | Type |
| Loyalty | Loyalty | 0.05 | 0.02 | 0.01 | 0.08 | 0.03 | 2.45 | .014 | Estim | Residuals |
| Satisfaction | Satisfaction | 0.23 | 0.09 | 0.05 | 0.41 | 0.13 | 2.45 | .014 | Estim | Residuals |
| Type1 | Type1 | 0.25 | 0.00 | 0.25 | 0.25 | 1.00 | Sample | Variables | ||
| Type1 | Service | 0.42 | 0.00 | 0.42 | 0.42 | 0.54 | Sample | Variables | ||
| Type1 | Food | 0.54 | 0.00 | 0.54 | 0.54 | 0.78 | Sample | Variables | ||
| Type1 | Income | 6.63 | 0.00 | 6.63 | 6.63 | 0.88 | Sample | Variables | ||
| Service | Service | 2.39 | 0.00 | 2.39 | 2.39 | 1.00 | Sample | Variables | ||
| Service | Food | 1.44 | 0.00 | 1.44 | 1.44 | 0.68 | Sample | Variables | ||
| Service | Income | 19.00 | 0.00 | 19.00 | 19.00 | 0.81 | Sample | Variables | ||
| Food | Food | 1.91 | 0.00 | 1.91 | 1.91 | 1.00 | Sample | Variables | ||
| Food | Income | 18.10 | 0.00 | 18.10 | 18.10 | 0.87 | Sample | Variables | ||
| Income | Income | 228.85 | 0.00 | 228.85 | 228.85 | 1.00 | Sample | Variables | ||
| Intercepts | ||||||
|---|---|---|---|---|---|---|
| 95% Confidence Intervals | ||||||
| Variable | Intercept | SE | Lower | Upper | z | p |
| Loyalty | 1.41 | 0.75 | -0.06 | 2.88 | 1.88 | 0.06 |
| Satisfaction | 4.04 | 1.17 | 1.76 | 6.33 | 3.46 | 0.00 |
| Type1 | 0.00 | 0.00 | 0.00 | 0.00 | ||
| Service | 5.67 | 0.00 | 5.67 | 5.67 | ||
| Food | 6.58 | 0.00 | 6.58 | 6.58 | ||
| Income | 49.25 | 0.00 | 49.25 | 49.25 | ||
| Contrasts Definition | |
|---|---|
| Name | Contrast |
| Type1 | 1 - 0 |
| Constraints input examples | ||
|---|---|---|
| Aim | Example | Outcome |
| Constraints | ||
| Equality constraint | p1==p2 | Constrain the estimates of p1 and p2 to be equal |
| Linear constraint | p1+p2==2 | Constrain the estimates of p1 and p2 to be equal to 2 |
| Linear constraint | p1+p2+p3==2 | Constrain the estimates for p1,p2, and p3 |
| Constrain coefficients | p1==0 | Fix the coefficient p1 to 0 |
| Inequality Constraint | p1>0 | Estimate the coefficient p1 as larger than 0 |
| Inequality Constraint | p1<3 | Estimate the coefficient p1 as smaller than 3 |
| Constrain intercepts | y1~0 | Fix the y1 intercept to 0 |
| Constrain intercepts | y1~1*0 | Fix the y1 intercept to 1 |
| Non linear constraint | p1*p2=0 | Constrain the estimates such that p1*p2 equals 0 |
| Defined Parameters | ||
| Linear estimates | p1+p2 | p1 and p2 are free, and their sum is estimated and tested |
| Linear estimates | (p1+p2)-p3 | p1,p2, and p3 are free, and the specified function is estimated and tested |
| Non linear estimates | p1*p2 | p1 and p2 are free, and their product is estimated and tested |
| Non linear estimates | ab:=p1*p2 | Estimate and test the product p1*p2 and name it `ab` |
| Non linear estimates | a2:=p1^2 | Estimate and test the square of p1 and name it `a2` |
| Free structural parameters | ||
| Estimate residual coovariances | y1~~y2 | Variables y1 and y2 covariance is set free |
| Estimate exogenous variables covariances | x1~~x2 | Variables x1 and x2 covariance is set free |
| Estimate exogenous variables variances | x1~~x1 | Variable x1 variance is set free |
| Estimate variables covariances | y1~~x1 | Variables y1 and x1 covariance is set free. Direct path should not be set |
| Estimate covariances involving interactions | x1:x2~~x3 | The interaction term x1:x2 and x3 variable covariance is set free. Direct path should not be set |
| Note. All the parameters labels are in the form `pN`, where `N` is a number. The parameter labels can be found in the results tables. Please be sure to have the options `Show parameters labels` selected. | ||
| Descriptives | ||||||
|---|---|---|---|---|---|---|
| Type | Service | Food | Income | Satisfaction | Loyalty | |
| N | 0 | 6 | 6 | 6 | 6 | 6 |
| 1 | 6 | 6 | 6 | 6 | 6 | |
| Mean | 0 | 4.83 | 5.50 | 36.00 | 5.67 | 6.83 |
| 1 | 6.50 | 7.67 | 62.50 | 7.83 | 9.00 | |
| Standard deviation | 0 | 0.75 | 0.55 | 6.36 | 0.82 | 0.75 |
| 1 | 1.87 | 1.21 | 9.35 | 0.75 | 0.89 | |
| Variance | 0 | 0.57 | 0.30 | 40.40 | 0.67 | 0.57 |
| 1 | 3.50 | 1.47 | 87.50 | 0.57 | 0.80 | |