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5 Example 1: Examining School Differences in Mathematics

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shows the first 15 cases for the data set used here, and TableÂ€13.3 shows descriptive

statistics. In TableÂ€13.2, schcode is the school ID (sorted from 1 to 419), and id is the

student ID variable. The student outcome is math, and ses is the student-level predictor. Note that some of the ses scores are negative, which is due to these scores being

centered around their respective school ses mean. At the school level, the focal variable of interest is the dichotomous public, with 73% of the schools in the sample being

public. The other school-level variable meanses, is included as a control variable, and

was formed by computing the mean of the uncentered (raw) student ses scores for the

students included in the sample from each of the given schools. Scores for mean ses

were then subsequently centered. Note that the student-level variables in TableÂ€13.2

vary within a school but the school-level variables are constant for each person within

a school. Also, note that even though we have variables at two different levels (student

and school), all of the variables appear in one dataÂ€file.

In addition, you might wonder why mean ses is needed in the analysis model, given

that we have a student ses variable. There are two primary reasons for this. First,

when student ses is group-mean centered, it cannot serve as a control variable for

any school-level predictor, because this form of centering makes the student predictor

uncorrelated with school predictors. As such, if we wish to use group-mean centering

for student ses and also control for ses differences between schools when we compare

public and private schools’ math performance, mean ses must be included as a predictor variable. Second, sometimes, the association between a predictor and an outcome

at level 1 (e.g., student ses and math) may differ from the association of these variables

at the school level (e.g., school mean ses and school mean math). When these associations differ, school mean ses is said to have contextual effect on math performance.

Table 13.2:â•‡ Data Set Showing First 15Â€Cases

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Table 13.3:â•‡ Variables and Descriptive Statistics for HLM Analysis

Variable

Variable name

Values

Mean

SD

57.73

0.00

8.78

6.07

0.73

0.00

0.44

4.94

Student-level

Math achievement

Socioeconomic status

Math

Ses

27.42 to 99.98

−21.71 to 24.10

School-level

School type

School ses

Public

Meanses

1Â€=Â€public, 0Â€=Â€other

−13.34 to 14.20

These within- and between-school associations, sometimes of intrinsic interest, are

estimated by including student ses and mean ses in the same analysis model. SectionÂ€13.6.1 discusses contextual effects in more detail.

In the analysis that follows, we assume that the researchers are interested primarily in

examining differences between public and private schools in math achievement. With

these data, researchers can not only examine whether public or private schools have,

on average, greater math achievement, but may also examine whether the association

between student ses and math is different for the two school types. What is desired,

perhaps, is to determine if there are schools where math performance is generally high

but that the ses-math slope is relatively small. Such a co-occurrence would indicate

that there are schools where students of varying ses values are all performing relatively

high in mathematics and that math performance does not depend in a great way on

student ses. If such schools are present, the analysis can then determine whether such

schools tend to be public or private.

13.5.1â•‡ The UnconditionalÂ€Model

Researchers often begin multilevel analysis with a completely unconditional model.

This model provides for us an estimate of the overall average across all students and

schools for the outcome (i.e., math), as well as an estimate of the variation that is

within and between schools for math. This modelÂ€is:

math ij = β0 j + rij , (7)

where the outcome math for student i in school j is modeled as a function of school

j’s intercept (β0j) and a residual term rij. Note that when no explanatory variables are

included on the right side of the model, the intercept becomes the average of the quantity on the left side. Thus, β0j represents a given school’s mathÂ€mean.

At level 2, school j’s intercept (or math mean) is modeled as function of a school-level

intercept and residual:

β0 j = γ 00 + u0 j (8)

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Again, with no predictors on the right side of the model, γ00 represents the average of

the school math means, or is sometimes referred to as the overall average. The school

random effect (i.e., u0j) represents the deviation of a given school’s math mean from

the overall math average. Note that the residual terms in EquationsÂ€7 and 8 are assumed

to be normally distributed, with a mean of zero, and have constant variance, with the

student- or within-school variance denoted by σ2 and the school-level variance denoted

by τ00. The student and school random effects (rij, u0j) are assumed to be uncorrelated.

As before, the combined model is formed by replacing the regression coefficients in

EquationÂ€7 with the right-hand side of EquationÂ€8. This modelÂ€is

math ij = γ 00 + u0 j + rij , (9)

where there is one fixed effect (γ00), a school-level random effect (u0j), and a student

random effect (rij), the latter of which is referred to as a residual (not random effect)

by SAS andÂ€SPSS.

TableÂ€13.4 shows the SAS and SPSS commands needed to estimate EquationÂ€9, and

TableÂ€13.5 shows selected analysis results. In TableÂ€13.5, the results from SAS and

SPSS are virtually identical with a couple of differences (i.e., degrees of freedom

for tests of fixed effects and p values reported for tests of variances). First, in the Fit

Statistics table in SAS and in the Information Criteria table of SPSS, −2 Restricted

Log Likelihood is a measure of lack of fit (sometimes referred to as model deviance),

estimated here to be 48,877.3. This value can be used to conduct a statistical test

for the intercept variance (τ00), which we will illustrate shortly. In the Fixed Effect

output tables, the intercept (γ00) is estimated to be 57.67, which is the overall math

average. Typically, the intercept would not be tested for significance, unless zero

is a value of interest as the null hypothesis is that γ00Â€=Â€0. Note that the degrees of

freedom associated with the test of the fixed effect differs between SAS (418) and

SPSS (416.066). West, Welch, and Galecki (2014) explain that t tests with multilevel

models do not exactly follow a t distribution. As a result, different methods are available to estimate a degrees of freedom for this test. The MIXED procedure in SPSS

uses the Satterthwaite method (by default and exclusively) to estimate the degrees of

freedom, with this method intended to provide more accurate inferences when small

sample sizes are present. SAS PROC MIXED has a variety of methods available to

estimate this degrees of freedom. While the Satterthwaite method can be requested

in SAS, the syntax in TableÂ€13.3 uses the default method (called containment), which

estimates the degrees of freedom based on the model specified for the random effects

(West et al., 2014, p.Â€131).

In the Covariance Parameters table of TableÂ€13.5, the student-level variance in

math is estimated to be 66.55, and the school-level math variance is estimated to be

10.64. The Wald z tests associated with these variances suggest that math variation

is present in the population within and between schools (the null hypothesis for

each variance is that it is zero in the population). Note that when using these z tests

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Table 13.4: SAS and SPSS Control Lines for Estimating the Completely Unconditional

Model

SAS

SPSS

(1) P

ROC MIXED METHODÂ€=Â€REML NOCLPRINT

COVTEST NOITPRINT;

(2) CLASS schcode;

(3) MODEL mathÂ€=Â€/ SOLUTION;

RANDOM intercept / SUBJECT=schcode;

(4)

RUN;

(5)

(6)

(7)

(8)

(9)

MIXED math

/FIXED=| SSTYPE(3)

/METHOD=REML

/PRINT=G SOLUTION TESTCOV

/RANDOM=INTERCEPT | SUB

JECT(schcode)COVTYPE(VC).

(1) PROC MIXED invokes the mixed modeling procedure; METHODÂ€=Â€REML requests restricted maximum likelihood estimation, NOCLPRINT suppresses printing of the number of schools,

COVTEST requests z tests for variance-covariance elements, and NOITPRINT suppresses printing of

information on iteration history.

(2) CLASS defines the cluster-level variable and must precede the MODEL statement.

(3) MODEL specifies that math is the dependent variable and no predictors are included, although the intercept (γ00) is included by default, SOLUTION displays fixed effects estimates in the output.

(4) RANDOM specifies random effects for the intercept and the identifier (schcode) indicates that students are

nested in schools. This line is omitted when a deviance test is used for τ00.

(5) MIXED invokes the mixed modeling procedure and math is then indicated as the dependent variable.

(6) FIXED indicates that no fixed effects are included in the model although the intercept (γ00) is included by

default. SSTYPE(3) requests the type 3 sum of squares.

(7) METHOD requests restricted maximum likelihood estimation.

(8) PRINT requests school-level variance components, the fixed effect estimates and tests, and statistical

test results for the variance parameters.

(9) RANDOM specifies random effects for the intercept and the identifier (schcode) indicates that students

are nested in schools, COVTYPE(VC) requests the estimation of the intercept variance (τ00). This line is

omitted when a deviance test is used for τ00.

for variances, Hox (2010) recommends that the obtained p values be divided by 2

because while this z test is a two-tailed test, variances must be zero or greater. It is

important to note that SAS provides these recommended p values, whereas SPSS

does not. So, p values obtained from SPSS for variances should be divided by 2

when assessing statistical significance. Given the small p values here, the results

indicate, then, that within school, student math scores vary and between schools

math means vary. Note though that these z tests provide approximate p values as

variances are not normally distributed. More accurate inference for variances can be

obtained by testing model deviances, which are generally preferred over the z tests

and is discussedÂ€next.

As is the case with other statistical techniques discussed in this book, statistical tests

that compare model deviances may often be conducted when maximum likelihood

estimation is used. With multilevel modeling, two forms of maximum likelihood

estimation are generally available in software programs: Full Maximum Likelihood

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Table 13.5:â•‡ Results From the UnconditionalÂ€Model

SAS

Fit Statistics

-2 Res Log Likelihood

AIC (smaller is better)

AICC (smaller is better)

BIC (smaller is better)

48877.3

48881.3

48881.3

48889.3

Solution for Fixed Effects

Effect

Intercept

Estimate

57.6742

Standard Error DF

0.1883

418

tÂ€Value

306.34

Pr > |t|

<.0001

Z Value

10.35

56.80

Pr > Z

<.0001

<.0001

Covariance Parameter Estimates

Cov Parm

Intercept

Residual

Subject

Schcode

Â€

Estimate

10.6422

66.5507

Standard Error

1.0287

1.1716

SPSS

Information Criteriaa

-2 Restricted Log Likelihood

Akaike’s Information Criterion (AIC)

Hurvich and Tsai’s Criterion (AICC)

Bozdogan’s Criterion (CAIC)

Schwarz’s Bayesian Criterion (BIC)

48877.256

48881.256

48881.257

48896.925

48894.925

The information criteria are displayed in smaller-is-better forms.

a

Dependent Variable: math.

Fixed Effects

Estimates of Fixed Effectsa

95% Confidence Interval

Parameter

Estimate

Std. Error Df

T

Sig.

Lower Bound Lower

Bound

Intercept

57.674234

.188266

306.344

.000

57.304162

a

416.066

58.044306

Dependent Variable: math.

Covariance Parameters

Estimates of Covariance Parametersa

95% Confidence Interval

Parameter

Estimate Std. Error Wald Z

Sig.

Lower Bound Lower

Bound

Residual

Intercept [sub- Variance

jectÂ€=Â€schcode]

66.550655 1.171618

10.642209 1.028666

.000

.000

64.293492

8.805529

a

Dependent Variable: math.

56.802

10.346

68.887062

12.861989

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(FML) and Restricted Maximum Likelihood (RML), with the latter preferred when

the number of clusters is relatively small because it provides for unbiased estimates

of variance and covariances. However, when RML is used, only variances and covariances (not fixed effects) may be properly tested with the deviance method. When

FML is used, both fixed effects and variance-covariance elements may be tested using

model deviances, although West etÂ€al. (2014, p.Â€36) recommend deviance tests of

variance-covariances be done with RML only and tests of fixed effects be conducted

with FML. In this example, RML, which is the default estimation procedure for SAS

and SPSS, is used for estimation.

To conduct a test using deviances to determine if the intercept varies across schools,

two models, one nested in the other, need to be estimated. Then, one obtains an overall

measure of model fit, the deviance, and computes the difference between the nested

and full model deviances. This difference, in effect, follows a chi-square distribution

with a given alpha level (i.e., .05) and degrees of freedom, where the latter is equal

to the difference in the number of parameters estimated between the full and nested

model. Note that since the intercept variance cannot be negative, Snijders and Bosker

(2012, p.Â€98) recommend halving the p values, which is the same as doubling the alpha

level used for the test (i.e., .10.)

To test the variance of the intercept (H0 : τ00Â€=Â€0) using deviances, the two comparison

models must be identical in terms of the fixed effects and can only differ in the variances estimated. Thus, to estimate an appropriate comparison model here, EquationÂ€7

is the level-1 model. The level-2 model is the same as EquationÂ€8 except there is no u0j

term in the model for β0j, as each u0j is constrained to be zero. As such, the variance of

β0j (i.e., τ00) in this new model is constrained to be zero. This new model, then, is nested

in the three-parameter model, represented by EquationÂ€9, and estimates two parameters: one fixed effect (like the previous model) but just one variance component, the

student-level variance (σ2). Note that to obtain the results for this nested model, you

use the same syntax as shown in TableÂ€13.4, except that the RANDOM subcommand

line is removed, which constrains τ00 toÂ€zero.

To complete the statistical test, we estimated this reduced two-parameter model and

found that the deviance, or the quantity −2 times the log likelihood, is 49,361.120,

whereas the original unconditional model deviance is 48,877.256 (as shown in

TableÂ€13.5). The difference between these deviances is 483.864, which is greater than

the corresponding chi-square value of 2.706 (.10, dfÂ€=Â€1). Therefore, we conclude that

the school math means vary in the population.

Summarizing the results obtained from this unconditional model, performance on the

math test is, on average, 57.7. Math scores vary both within and between schools.

Inspecting the variance estimates indicates that a majority of math variance is within

schools. In this two-level design, the intraclass correlation provides a measure of the

proportion of variability in the outcome that exists between clusters. For the example

here, the intraclass correlation provides a measure of the proportion of variability in

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math that is between schools. The formula for the intraclass correlation for a two-level

modelÂ€is:

ρICC =

τ 00

(10)

τ 00 + σ 2

For the current data set, the intraclass correlation estimate thenÂ€is

ρICC =

τ 00

10.642

=

= .138. (11)

2

10.642 + 66.551

τ 00 + σ

Thus, about 14% of the variation in math scores is between schools. According to

Spybrook and Raudenbush (2009, p.Â€304), the intraclass correlation for academic outcomes in two-level educational research with students nested in schools is often in

the range from 0.1 to 0.2, which is consistent with the data here and suggests that an

important part of the math variation is present across schools.

13.5.2â•‡ Random-CoefficientÂ€Model

A second model often used in multilevel analysis is the random-coefficient model. In

this model, one or more predictors are added to the level-1 model, and the lower-level

intercept and slope for at least one of the predictors are specified to vary across clusters. In this example, student ses will be included as a predictor variable and we will

determine if the association between ses and math varies across schools. The level-1

or student-level modelÂ€is

(

)

mathij = β 0 j + β1 j sesij - ses j + rij , (12)

where group-mean centered ses is now included as a predictor at level 1. As discussed

in sectionÂ€ 13.4, with group-mean centering, β0j represents a given school j’s math

mean, and β1j represents the within-school association between ses and math. The

student-level residual term represents the part of the student-level math score that is

not predictable by ses, and rij ~ N(0, σ2).

In the school-level model, the regression coefficients of EquationÂ€12 serve as outcome

variables and no school-level predictors are included. This modelÂ€is

β0 j = γ 00 + u 0 j (13)

,

β1 j = γ 10 + u1 j

where the two fixed effects (i.e.,γ00 and γ10) represent the overall math average and

overall average of the student-level slopes relating ses to math. We allow the residual

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terms to vary and covary, as in EquationÂ€4. The combined expression for the multilevel

model isÂ€then

(

)

(

)

mathij = γ 00 + γ 10 sesij - ses j + u0 j + u1 j sesij - ses j + rij . (14)

TableÂ€13.6 shows the syntax that can be used to estimate EquationÂ€14 using SAS

and SPSS. TableÂ€13.7 shows selected SPSS results, as results from SAS, as we have

seen, are very similar. In TableÂ€13.7, the deviance for the random-coefficient model is

48,479.875. Recall that since RML was used, we cannot use this deviance to test any

hypotheses associated with the fixed effects. However, we will use this deviance to test

the slope variance (i.e., τ11). The estimates of the fixed effects are that the mean math

score is 57.7, and the average of the within-school ses-math slopes is .313, indicating

that student math scores increase by about .3 points as student ses increases by 1 point.

The corresponding t test (tÂ€=Â€18.759) and p value (< .001) for this association indicates

a positive association is present in the population.

For the variance and covariance estimates, we begin with the student-level residual variance in TableÂ€13.7, which is 62.18 (p < .001), indicating that significant

student-level variance in math remains after adding ses. The estimates for the school

variance-covariance components are readily seen in the last output table in TableÂ€13.7,

which is the variance-covariance matrix for the school random effects. This table

shows that the variance in math means between schools is 10.91, the variance in

slopes is .01, and the covariance between the school math means and ses-math slopes

Table 13.6: SAS and SPSS Control Lines for Estimating the Random-CoefficientÂ€Model

SAS

SPSS

PROC MIXED METHODÂ€=Â€REML NOCLPRINT

COVTEST NOITPRINT;

CLASS schcode;

MODEL mathÂ€=Â€ses / Â�

SOLUTION;

(1)

RANDOM intercept ses / typeÂ€=Â€un

(2)

SUBJECT=schcode;

RUN;

(3) MIXED math WITH ses

/FIXED= ses | SSTYPE(3)

(4)

/METHOD=REML

/PRINT=G SOLUTION TESTCOV

/RANDOM=INTERCEPT ses |

(5)

Â�SUBJECT(schcode) COVTYPE(UN).

(1) The MODEL statement adds ses as a predictor variable.

(2) The RANDOM statement specifies that random effects appear in the model for the school math means and

the ses-math slopes; typeÂ€=Â€un specifies that a variance-covariance matrix be estimated for the school random

effects. Note that removing ses from this statement would specify a random intercept model, which constrains

τ11 and τ01 toÂ€zero.

(3) The MIXED statement indicates that ses is included as a covariate.

(4) The FIXED statement requests that a fixed effect be estimated forÂ€ses.

(5) The RANDOM statement specifies that random effects appear in the model for the school math means

and the ses-math slopes; COVTYPE(UN) specifies that a variance-covariance matrix be estimated for the

school random effects. Note that removing ses from this statement would specify a random intercept model,

which constrains τ11 and τ01 toÂ€zero.

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Table 13.7: SPSS Results From the Random-CoefficientÂ€Model

Information Criteriaa

-2 Restricted Log Likelihood

Akaike’s Information Criterion (AIC)

Hurvich and Tsai’s Criterion (AICC)

Bozdogan’s Criterion (CAIC)

Schwarz’s Bayesian Criterion (BIC)

48479.875

48487.875

48487.881

48519.215

48515.215

The information criteria are displayed in smaller-is-better forms.

a

Dependent Variable: math.

Fixed Effects

Estimates of Fixed Effectsa

95% Confidence Interval

Parameter

Estimate

Std. Error

Df

t

Sig.

Intercept

Sesgrpcen

57.675771

.312781

.188222

.016674

416.090

384.194

306.425

18.759

.000

.000

a

Lower Bound

Upper

Bound

57.305787

.279998

58.045755

.345565

Dependent Variable: math.

Covariance Parameters

Estimates of Covariance Parametersa

95% Confidence Interval

Parameter

Estimate

Std. Error Wald Z Sig.

Residual

62.176171 1.122366 55.397 .000

UN 10.909371 1.028421 10.608 .000

Intercept +

sesgrpcen [sub- (1,1)

-.162162

.067697 -2.395 .017

jectÂ€=Â€schcode] UN

(2,1)

UN

.011194

.007102 1.576 .115

(2,2)

a

Lower Bound

Upper Bound

60.014834

9.068958

64.415345

13.123270

-.294846

-.029477

.003228

.038814

Dependent Variable: math.

Random Effect Covariance Structure (G)a

Intercept | schcode

Intercept | schcode

sesgrpcen | schcode

Unstructured

a

Dependent Variable: math.

10.909371

-.162162

sesgrpcen | schcode

-.162162

.011194

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is −.16. The correlation, then, between school math means and ses-math slopes is

-.16 10.91 × .01 = -.48. This negative correlation indicates that schools with higher

math means tend to have flatter ses-math slopes, suggesting that math performance in

some schools is relatively high and more equitable for students having various ses values. Note that the value of the slope variance (.01) is not, perhaps, readily interpretable

and in an absolute sense seems small. To render the slope variance more meaningful,

we can compute the expression γ 10 ± 2 × τ11 , which obtains values of β1j that are 2

standard deviations above and below the mean slope value. For these data, these slope

values are .113 and .513. Thus, this suggests that there are schools in the sample where

the ses-math slope is fairly small (about a .11 increase in math for a point change

in ses), whereas this association in other schools is stronger (about a .51 increase in

math for a point change in ses). Further, using the z tests, the p values provided in the

Covariance Parameters table indicate that the variance in the math means (p < .001)

and the covariance of the math means and ses-math slopes (pÂ€=Â€.017) is significant at

the .05 level but that the variance in ses-math slopes is not (p / 2Â€=Â€.115 / 2Â€=Â€.058). As

discussed, these z tests do not provide as accurate inference as deviance tests, so in the

next section we consider using a deviance test to assess the variance-covariance terms

associated with the slope.

FigureÂ€13.2 provides a visual depiction of these results. This plot shows predicted math

scores for each of 50 schools as a function of student ses (with 50 schools selected

instead of all schools to ease viewing). Given that ses is group-mean centered, the

mean math score for a given school is located on the regression line above an ses score

77.0

67.0

Math

57.0

47.0

37.0

27.0

20.0

10.0

0.0

SES

10.0

Figure 13.2 Predicted math scores as a function of ses for each of 50 schools.

20.0

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of zero. Examining the plot suggests that these mean math scores vary greatly across

schools. In addition, the plot also suggests that the math-ses slopes vary across schools

as some slopes are near zero, while others are mostly positive. Also, the negative correlation between the math means and math-ses slopes is evident in that schools having

predicted math scores greater than 57 when ses is zero tend to have slopes near zero

(flat slopes), whereas other schools (with lower mean math scores) tend to have positive math-ses slopes.

13.5.3â•‡Deviance Test for a Within-School Slope Variance and

Covariance

Previously, we showed how model deviances can be used to test a single variance

(e.g., τ00). We now show how model deviances can be used to test the variance and

covariance associated with adding a random effect for a within-school slope. As

before, we compare the deviance from two models, where one model is nested in

the other. The random-coefficient model (i.e., the full model) has already been estimated, and this model includes six parameters: two fixed effects (γ00 and γ01) and four

variance-covariance terms, that is, the student-level variance (σ2), the variance of the

math means (τ00), the slope variance (τ11), and the covariance between the math means

and ses-math slopes (τ01). The nested model that we will estimate will constrain the

slope variance (τ11) to zero and by doing so will also constrain the covariance (τ01)

toÂ€zero.

Recall that when testing variance-covariance terms, the two comparison models must

have the same fixed effects. Thus, for this reduced model, EquationÂ€12 remains the

student-level model. In addition, EquationÂ€13 is the school-level model, except that

there is no u1j term in the model for β1j, as each u1j is constrained to be zero (which

then constrains τ11 and τ01 to zero). Thus, the reduced model has four parameters: the

same two fixed effects as the random-coefficient model, but just two variances: the

student-level variance (σ2) and the variance of the math means (τ00). Note that this

random intercept model can be estimated with SAS and SPSS by removing ses from

the respective RANDOM statement from the syntax in TableÂ€13.6.

We estimated the random intercept model to conduct this deviance test. The estimate of the deviance from the random intercept model is 48,488.846, whereas the

random-coefficient model returned a deviance of 48,479.875. The difference between

these deviances is 8.971. AÂ€key difference between the deviance test of a single variance (as illustrated in sectionÂ€13.5.1) and the test of the variance and covariance here is

that this test statistic is not distributed as a standard chi-square test (SnijdersÂ€& Bosker,

2012, p.Â€99; West etÂ€al. 2014, p.Â€36). Instead, this test statistic follows a chi-bar distribution, which is a mix of chi-square distributions having different degrees of freedom.

Snijders and Bosker (2012, p.Â€99) provide selected critical values for such a distribution, and we use a critical value from their text given an alpha of .05 and when the

slope variance and covariance for a single predictor (here, ses) is being tested, with this

critical value being 5.14. Given in our example that the test statistic of 8.971 exceeds

## 2016 keenan a pituch, james p stevens applied multivariate statistics for the social sciences analyses with SAS and IBMs SPSS routledge (2015)

## 2 Type I Error, Type II Error, and Power

## 2 Addition, Subtraction, and Multiplication of a Matrix by a Scalar

## K-Group MANOVA: A Priori and Post Hoc Procedures

## 14 Power Analysis—A Priori Determination of Sample Size

## 9 MANCOVA—Several Dependent Variables and Several Covariates

## 10 McFadden’s Pseudo R-Square for Strength of Association

## 9 Sample Size for Power = .80 in Single-Sample Case

## 8 Example 2: Evaluating the Efficacy of a Treatment

## 6 Example 1: Using SAS and SPSS to Conduct Two-Level Multivariate Analysis

## 7 Example 2: Using SAS and SPSS to Conduct Three-Level Multivariate Analysis

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