A structural equation modeling approach for examining position effects in largescale assessments
Bulut et al. Largescale Assess Educ
A structural equation modeling approach for examining position effects in large‑scale assessments
Okan Bulut
Qi Quo
Mark J. Gierl
Position effects may occur in both paperpencil tests and computerized assessments when examinees respond to the same test items located in different positions on the test. To examine position effects in largescale assessments, previous studies often used multilevel item response models within the generalized linear mixed modeling framework. Using the equivalence of the item response theory and binary factor analysis frameworks when modeling dichotomous item responses, this study introduces a structural equation modeling (SEM) approach that is capable of estimating various types of position effects. Using real data from a largescale reading assessment, the SEM approach is demonstrated for investigating form, passage position, and item position effects for reading items. The results from a simulation study are also presented to evaluate the accuracy of the SEM approach in detecting item position effects. The implications of using the SEM approach are discussed in the context of largescale assessments.
Position effect; Largescale assessment; Structural equation modeling; Item response theory

Largescale assessments in education are typically administered using multiple test
forms or booklets in which the same items are presented in different positions or
locations within the forms. The main purpose of this practice is to improve test security
by reducing the possibility of cheating among test takers (Debeer and Janssen 2013).
This practice also helps test developers administer a greater number of fieldtest items
embedded within multiple test forms. Although this is an effective practice for ensuring
the integrity of the assessment, it may result in context effects—such as an item position
effect—that can unwittingly influence the estimation of item parameters and the latent
trait (Bulut 2015; Hohensinn et al. 2011). For example, test takers may experience either
increasing item difficulty at the end of the test due to fatigue or decreasing item difficulty
due to testwiseness as they become more familiar with the content (Hohensinn et al.
It is often assumed that position effects are the same for all test takers or for all items
and thus do not have a substantial impact on item difficulty or test scores (Hahne 2008).
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However, this assumption may not be accurate in operational testing applications, such
as statewide testing programs. For example, in a reading assessment where items are
often connected to a reading passage, it is challenging to maintain the original
positions of the items from field test to final form to eliminate the possibility of any
position effects (Meyers et al. 2009). Similarly, the positions of items cannot be controlled
in computerized adaptive tests in which item positions typically differ significantly from
one examinee to another (Davey and Lee 2011; Kolen and Brennan 2014; Meyers et al.
2009). Therefore, nonnegligible item position effects may become a source of error in
the estimation of item parameters and the latent trait (Bulut et al. 2016; Hahne 2008).
Several methodological studies have focused on item position effects in national and
international largescale assessments, such as the Trends in International
Mathematics and Science Study (Martin et al. 2004), a mathematics assessment of the Germany
Educational Standards (Robitzsch 2009), a mathematical competence test of the
Austrian Educational Standards (Hohensinn et al. 2011), the Graduate Record Examination
(Albano 2013), the Program for International Student Assessment (Debeer and Janssen
2013; Hartig and Buchholz 2012), and a German nationwide largescale assessment in
mathematics and science (Weirich et al. 2016). In these studies, item position effects
were estimated as either fixed or random effects using multilevel item response theory
(IRT) models within the generalized linear mixed modeling framework. This approach is
also known as explanatory item response modeling (De Boeck and Wilson 2004), which
is equivalent to Rasch modeling (Rasch 1960) with explanatory variables.
An alternative way of estimating IRT models is with a binary factor analysis (FA)
model in which the item parameters and latent trait can be estimated using tetrachoric
correlations among dichotomous item responses (Kamata and Bauer 2008; McDonald
1999; Takane and de Leeuw 1987). In addition to estimating the item parameters and
latent trait, the binary FA model can be expanded to a structural equation model (SEM)
in which item difficulty parameters can be predicted by other manifest (i.e., observed)
variables (e.g., item positions, passage or testlet positions, and indicators of test forms).
The purpose of this study is to (1) introduce the SEM approach for detecting position
effects in largescale assessments; (2) demonstrate the methodological adequacy of the
SEM approach to model different types of position effects using an empirical study; and
(3) examine the accuracy of the SEM approach in detecting position effects using a
simulation study.
Theoretical framework
IRT and factor analysis
The FA model is a regression model in which the independent variables are latent
variables and the dependent variables are manifest variables (Ferrando and LorenzoSeva
2013). Previous studies on the relationship between the FA and IRT frameworks showed
that the unidimensional twoparameter normal ogive IRT model is equivalent to a
onefactor FA model when binary manifest variables are predicted by a continuous latent
variable (e.g., Brown 2006; Ferrando and LorenzoSeva 2005; GlöcknerRist and Hoijtink
2003; MacIntosh and Hashim 2003; Takane and de Leeuw 1987). The twoparameter
normal ogive IRT model can be written as
where yi is the dichotomous item responses for item i, θ is the latent trait (i.e., ability),
ai is the item discrimination parameter, bi is the item difficulty parameter, and is the
standard normal cumulative distribution function. Using the parameterization described
in Asparouhov and Muthén (2016), the onefactor FA model for binary manifest
variables that corresponds to the twoparameter IRT model in Eq. 1 can be written as follows:
where yi∗ is the continuous latent response underlying the observed item response for
item i, νi is the intercept term for item i, which is typically assumed to be zero, i is the
factor loading for item i, which is analogous to item discrimination in traditional IRT
models, η is the latent trait, which is often assumed to follow a normal distribution as
η ∼ N (0, 1)1, and εi is the residual term2 for item i (for compactness in the notation, no
person subscript is included). In case of the Rasch model (Rasch 1960) or the
oneparameter IRT model, factor loadings in the onefactor FA model are fixed to “1” or
constrained to be equal across all items.
In the onefactor FA model, there is also a threshold parameter (τi) for each
dichotomous item, which corresponds to item difficulty (bi in Eq. 1) in traditional IRT models.
Based on the threshold parameter, an observed item response yi becomes
yi =
Although factor loadings and intercepts in the onefactor FA model are analogous to
item discrimination and item difficulty in IRT, these factor analytic terms can be
formally transformed into item parameters in the traditional IRT scale (Asparouhov and
Muthén 2016; Muthén and Asparouhov 2002). Assuming that the latent trait is
normally distributed as η ∼ N (α, ψ ) and η = α + √ψ θ where θ is the IRTbased latent
trait with mean 0 and standard deviation 1, the item discrimination parameter can
be computed as ai = i/√ψ , and the item difficulty parameter can be computed as
bi = (τi − iα)/ i√ψ [see Brown (2006) and Takane and de Leeuw (1987) for a review
of similar transformation procedures].
Modeling position effects
As Brown (2006) noted, the use of the FA model provides greater analytic flexibility than
the IRT framework because traditional IRT models can be embedded within a larger
model that includes additional variables to explain the item parameters as well as the
latent trait (e.g., Ferrando et al. 2013; GlöcknerRist and Hoijtink 2003; Lu et al. 2005).
Using the structural equation modeling (SEM) framework, an IRT model can be defined
as a measurement model in which there is a latent trait (e.g., reading ability)
underlying a set of manifest variables (e.g., dichotomous items in a reading assessment). In the
structural part of the SEM model, the causal and correlational relations among the latent
trait, the manifest variables, and other latent or manifest variables (e.g., gender and
attitudes toward reading) can therefore be tested.
1 The mean and variance are fixed to 0 and 1, respectively, to identify the scale of the latent variable.
2 The residuals are typically assumed to have a normal distribution (Kamata and Bauer 2008).
A wellknown example of this modeling framework is the Multiple Indicators
Multiple Cause (MIMIC) model for testing uniform and nonuniform differential item
functioning in dichotomous and polytomous items (e.g., Finch 2005; Lee et al. 2016; Woods
and Grimm 2011). In the MIMIC model, a categorical grouping variable (e.g., gender)
is used as an explanatory variable to explain the relationship between the probability of
responding to an item correctly and the grouping variable, after controlling for the latent
trait. Based on the results of this analysis, one can conclude that the items become
significantly less or more difficult depending on which group the examinee belongs to.
In the current study, instead of a categorical grouping variable, item position will be
used as a continuous, explanatory variable to predict whether there is any relationship
between item difficulty and the varying positions of the items on the test. Using the FA
model in Eq. 2, the SEM model for examining linear item position effects can be written
as follows:
where pi is a manifest variable that shows the positions of item i across all examinees
who responded to item i (e.g., 1, 15, 25, and 50 if item i is administered at the 1st, 15th,
25th, or 50th positions), βi is the linear position effect based on one unit change in the
position of item i, and the remaining terms are the same as those from Eq. 2. In Eq. 3, if
βi = 0, it can be concluded that the difficulty of item i does not depend on its position
on the test. If, however, βi = 0, then the difficulty of item i either linearly increases or
decreases as a function of its position on the test (i.e., pi).
It is also possible to extend the SEM model in Eq. 3 to examine the interaction between
the latent trait and item position, if the relation between the difficulty of an item and its
position on the test is assumed to be moderated by the latent trait. To estimate the
interaction effect, an interaction term as a product of item position and the latent trait needs
to be included as an additional predictor in the model (Moosbrugger et al. 2009). The
SEM model for examining linear position effects and interaction effects can therefore be
written as follows:
where pi is the positions of item i across all examinees who responded to item i, βi is the
linear position effect, ωi is the interaction effect for item i, and the other terms are the
same as those in Eq. 3. If, ωi = 0, then the difficulty of item i increases or decreases as
a result of the interaction between the latent trait and item positions (i.e., ηpi in Eq. 4).
There are several methods used for estimating the interaction between the latent trait
and manifest variables (e.g., item and testlet positions), such as the Latent Moderated
Structural Equations (LMS) approach (Klein and Moosbrugger 2000), the
QuasiMaximum Likelihood (QML) approach (Klein and Muthén 2007), the constrained approach
(Jöreskog and Yang 1996), and the unconstrained approach (Marsh et al. 2004). The
examples of interaction effects in SEM with binary manifest variables can be found in
Woods and Grimm (2011) and Lee et al. (2016).
Figure 1 shows a path diagram of an SEM model based on k dichotomous items. In the
model, item 1 is tested for linear position effects and interaction effects. Although Fig. 1
only shows the estimation of item position effect for item 1, all items on the test (or a
Fig. 1 The structural equation model for examining item position effects (Note η = Latent trait; 1to k =
factor loadings; τ1to τk = item thresholds; p1= positions of item 1 on the test; β1= linear item position effect for
item 1; ω1= interaction term between latent trait and positions of item 1)
group of items) could be evaluated together for item position effects in the same model.
It should be noted that the correlations between item positions and the latent trait (i.e.,
a doubleheaded arrow as in Fig. 1) are optional. If item positions are randomly
determined for each examinee (i.e., independent of examinees’ latent trait levels), then the
correlation between item positions and the latent trait can be excluded from the model
for the sake of model parsimony. However, the ordering of items can sometimes depend
on examinees’ latent trait level. For example, in a computerized adaptive test, two
examinees can receive the same item at different positions because of how they responded to
earlier items on the test (e.g., van der Linden et al. 2007). Furthermore, in a fixedform
and nonadaptive test, examinees can still start and exit the test with different items,
depending on test administration rules related to examinees’ response patterns (e.g.,
Bulut et al. 2016; Li et al. 2012). When examining item position effects in such
assessments, a correlational link between item positions and the latent trait should be included
in the model to account for this dependency.
Model constraints and assumptions
The proposed SEM approach requires the use of constraints to ensure model
identification and accurate estimation of model parameters. Using the marginal parameterization
with a standardized factor (Asparouhov and Muthén 2016; Millsap and YunTein 2004;
Muthén and Asparouhov 2002), the variance of yi∗ is constrained to be 1 for all items; the
mean and the variance of the latent trait (η) are constrained to be 0 and 1, respectively
[see Kamata and Bauer (2008) for other parameterizations in the binary FA model].
Furthermore, additional constraints might be necessary depending on which the IRT model
is used. For example, if the Rasch model is the underlying IRT model for the data, all
factor loadings in the model must be constrained to be 1. The general assumptions of
IRT must also hold in order to estimate the IRT model with item position effects within
the SEM framework. These assumptions include a monotonic relationship between the
probability of responding to an item correctly and the latent trait, the unidimensionality
of the latent trait, local independence of items, and invariance in the item parameters
and the latent trait across different subgroups in a population.
Model estimation
The proposed SEM models for examining item position effects can be estimated using
commercial software programs for the SEM analysis, such as Mplus (Muthén and
Muthén 1998–2015), LISREL (Jöreskog and Sörbom 2015), AMOS (Arbuckle 2011),
and EQS (Bentler and Wu 2002), or noncommercial software programs, such as the
sem (Fox et al. 2016), lavaan (Rosseel 2012), OpenMx (Pritikin et al. 2015), and nlsem
(Umbach et al. 2017) packages in R (R Core Team 2016). It should be noted that these
software programs differ with regard to their algorithms for estimating SEM models
with binary variables, model estimators (e.g., ULS, MLR, and WLSMV), the capability
to estimate interaction effects, and methods for handling missing data. Therefore, when
choosing the most suitable program, researchers should consider the research
questions that they aim to address, the statistical requirements of their hypothesized SEM
model(s), and data characteristics (e.g., the number of items, amount of missing data).
As noted earlier, item position effects can be examined one item at a time using Eqs. 3
and 4. However, it is more convenient to estimate the position effects for all items within
the same SEM model and then create a simplified model by removing nonsignificant
effects from the model. Since the simplified model would be nested within the
original model that includes the position effects for all items, the two models can be
compared using a Chi square (χ 2) difference test. The procedure for the χ 2 difference test
varies depending on which model estimator (e.g., the robust maximum likelihood
estimator ‘MLR’ or the weighted least square with mean and varianceadjusted Chi square
‘WLSMV’ in Mplus) is used for estimating the SEM model. For example, the χ 2
difference test using the corrected loglikelihood values or the Satorra–Bentler χ 2
statistic (Satorra and Bentler 2001) are widely used when the model estimator is MLR. The
reader is referred to Brown (2006, p. 385), Asparouhov and Muthén (2006), and the
Mplus website (www.statmodel.com/chidiff.shtml) for detailed descriptions of the χ 2
difference testing in SEM. When the SEM models are not nested, the model selection
or comparison can be done on the basis of informationbased criteria that assess
relative model fit, such as Akaike information criterion (AIC; Akaike 1974) and the Bayesian
information criterion (BIC; Schwarz 1978).
Comparison with other approaches
To date, three different methodological approaches for investigating item position effects
have been described: (1) logistic regression models (e.g., Davey and Lee 2011; Pomplun
and Ritchie 2004; Qian 2014); (2) multilevel models based on the generalized linear
mixed modeling (GLMM) framework (e.g., Albano 2013; Alexandrowicz and
Matschinger 2008; Debeer and Janssen 2013; Hartig and Buchholz 2012; Li et al. 2012; Weirich
et al. 2014); and (3) test equating methods (e.g., Kingston and Dorans 1984; Kolen and
Harris 1990; Moses et al. 2007; Pommerich and Harris 2003; Meyers et al. 2009; Store
2013). Although there are some empirical studies that used the factor analytic methods
for modeling position effects (e.g., Bulut et al. 2016; Schweizer 2012; Schweizer et al.
2009), the current study represents the first study that utilized the SEM framework as a
methodological approach for examining item position effects.
The proposed SEM approach has four noteworthy advantages over the other methods
mentioned above when it comes to modeling item position effects. First, the proposed
approach overcomes the limitation of examining position effects only for dichotomous
items, which is the case with the approaches based on the GLMM framework (e.g.,
Hartig and Buchholz 2012). Using the SEM framework, assessments that consist of
polytomously scored items can also be examined for item position effects. Second, the
proposed approach is applicable to assessments in which item parameters are obtained
using the twoparameter IRT model. Because the onefactor FA model is analogous to
the twoparameter IRT model, it is possible to estimate item position effects when both
item difficulty and item discrimination parameters are present in the model. Third, the
proposed approach can be used with multidimensional test structures in which there are
multiple latent traits underlying the data. Fourth, once significant item position effects
are detected, other manifest and/or latent variables (e.g., gender, test motivation, and
test anxiety) can be incorporated into the SEM model to explain the underlying reasons
of the found effects. For example, Weirich et al. (2016) recently found in an empirical
study that item position effects in a largescale assessment were affected by the
examinees’ testtaking efforts. Response time effort (Wise and Kong 2005) and disability
status (Abedi et al. 2007; Bulut et al. 2016) are other important factors highlighted in the
literature.
Next, the results from an empirical study first are presented to demonstrate the use
of the proposed SEM approach for investigating three types of position effects that
are likely to occur in largescale assessments: test form (or booklet) effect, passage (or
testlet) position effect, and item position effect. Real data from a largescale reading
assessment are used for the empirical study. The interpretation of the estimated
position effects and model comparisons are explained. Then, the results from a Monte Carlo
simulation study are presented to investigate the extent to which the proposed SEM
approach can detect item position effects accurately. For both the empirical and Monte
Carlo studies, Mplus (Muthén and Muthén 1998–2015) is used because of its flexibility
to formulate and evaluate IRT models within a broader SEM framework and its
extensive Monte Carlo simulation capabilities (e.g., GlöcknerRist and Hoijtink 2003; Lu et al.
2005).
Empirical study
Data
Position effects were evaluated using data from a largescale statewide reading
assessment administered annually to all students in elementary, middle, and high schools. The
assessment was delivered as either a computerbased test or a paperandpencil test,
depending on the availability of computers in the schools. The paperandpencil
version was administered to the students using a single test form that consisted of the same
items in the same positions. Unlike the paper–pencil version, the computerbased
version was administered to the students by randomizing the positions of the items across
the students. The sample used in this study consisted of 11,734 thirdgrade students who
completed the reading test of the statewide assessment on a computer.
Instrument
The reading test consisted of 45 multiplechoice items related to seven reading passages.
The positions of reading passages and items varied across students, while the positions
of the items within each passage remained unchanged. This process resulted in four
different patterns of item positions on the test, which were referred to as test forms in
this study. Table 1 shows the demographic characteristics of the students across the
four forms. The forms were similar in terms of sample sizes and students’ demographic
characteristics.
Model formulations
Four SEM models were specified and analyzed. The first model (M1) was the baseline
model because it did not include any predictors for item position effects. M1 was
equivalent to the Rasch model because the item parameters in the selected reading assessment
were originally calibrated using the Rasch model. The remaining three models (i.e., M2
to M4) were designed to evaluate form effects, passage position effects, and item
position effects, respectively. The proposed models are shown in Fig. 2. In each model, a
unidimensional latent variable (i.e., reading ability) predicted a set of manifest variables (i.e.,
reading items). For models M2–M4, there were also additional predictors for the form,
passage position, and item position effects.
Rasch model (M1)
The first model was the Rasch model. It did not include any predictors for examining
positions effects. Under the SEM framework, M1 is equivalent to a onefactor model
with all factor loadings fixed to 1 (see Fig. 1a). The latent variable defines the reading
ability and the intercepts of the items are equivalent to item difficulties.
Form effects model (M2)
This model examined the form effects using indicators of test forms as categorical
predictors. The SEM formulation of M2 can be written as follows:
Table 1 Demographic summary of the students across test forms
yi∗ = iη + β1form1 + β2form2 + β3form3 + εi,
where form1, form2, and form3 are the dummy codes for the three test forms (using the
fourth test form as the reference form), β1, β2, and β3 are the regression coefficients that
represent the differences between the overall difficulty of the three test forms and the
reference test form. Note that β1, β2, and β3 do not have the subscript for items,
indicating that the form effects were constrained to be the same across all items. Under the
SEM framework, M2 is equivalent to a onefactor model with all factor loadings fixed to
1 and with form1 to form3 as additional predictors (see Fig. 1b).
Passage position effects model (M3)
This model examined passage position effects by incorporating passage positions into
the SEM model as predictors. Given item i belongs to passage h, the SEM formulation of
M3 can be shown as
where passage positionh is the position of passage h across all examinees and βh is the
regression coefficient that represents the impact of one unit change in the position of
passage h on the difficulty of items linked to passage h. Note that the passage position
effect was constrained to be the same for all items linked to passage h. Under the SEM
Table 2 The layout of the data structure for the SEM analysis
framework, M3 is equivalent to a onefactor model with all factor loadings fixed to 1 and
with passage positionh as an additional predictor (see Fig. 1c).
Item position effects model (M4)
The last model examined individual item position effects by incorporating item
positions into the model as predictors. The SEM formulation of M4 is given as:
where item positioni is the position of item i across all examinees, and βi is the
regression coefficient that represents the impact of one unit change in the position of item i
on the difficulty of item i. Unlike the first three models summarized above, this model
allows the effect of item position on each item to vary across items. Under the SEM
framework, M4 is equivalent to a onefactor model with all factor loadings fixed to 1 and
with item positioni as an additional predictor (see Fig. 1d).
Model estimation
Table 2 demonstrates the layout of the data structure for estimating the four SEM
models (M1 to M4) summarized above. The SEM models were estimated using the maximum
likelihood estimation with robust standard errors ‘MLR’ in Mplus 7 (Muthén and
Muthén 1998–2015). The MLR estimator was chosen over the maximum likelihood
estimator because it adjusts the standard errors of the parameter estimates when the data
do not meet the multivariate normality assumption, which is often the case with binary
and ordinal manifest variables (Li 2016). Mplus also provides alternative model
estimators—such as WLSMV3 and MLM—for the estimation of FA and SEM models with
binary manifest variables [see Brown (2006), chapter 9, for a discussion of the estimators
for categorical data]. The Mplus codes for estimating the passage and item position
effects are provided in “Appendices 1 and 2”.
All of the SEM models converged in less than two minutes. The estimated regression
coefficients for the form, passage, and item position effects were evaluated. Then, the
baseline model (i.e., M1) was compared to the other SEM models (M2 to M4) using a
Chi square difference test based on loglikelihood values and scaling correction factors
obtained from the SEM models. The scaling correction term (ccorrection) and the adjusted
Chi square value (χa2djusted) for the difference test can be computed as follows:
3 When the WLSMV estimator was used, the SEM models in the empirical study did not converge due to high correla
tions among passage and item position variables.
ccorrection =
with dfadjusted = df0 − df1 ,
where df is the number of parameters in the model, c is the scaling correction factor, L
is the loglikelihood value from the estimated model, and subscripts 0 and 1 represent
the baseline (i.e., M1) and the comparison models (either M2, M3, or M4), respectively.
Finally, the relative fit of the nonnested SEM models (M2, M3, and M4) was compared
using the AIC and BIC modelfit indices.
Results
Rasch model
The Rasch model (M1) was the baseline model without any predictors for form, passage
position, or item position effects. Table 3 shows the descriptive statistics of the latent
trait estimates obtained from the Rasch model across the four test forms. The
distributions of the latent trait from the four test forms were similar when the form effects,
passage position effects, and item position effects were ignored.
Form effects
M2 included the indicators for the test forms in the SEM model to examine the overall
differences in the item difficulty levels across the four forms. Using one form as the
reference form, the estimated regression coefficients indicated the overall difficulty
differences between the reference form and the other three forms. The estimated regression
coefficients for the form effects ranged from −0.032 to 0.032. None of the form effects
was statistically significant at the alpha level of α = .05, suggesting that the forms did not
differ in terms of their overall difficulty.
Passage position effects
Table 4 shows the estimated passage positions effects from M3. The results indicated that
passages 1, 4, and 5 showed significant passage position effects (−.029, −.044, and −.08,
respectively). For example, if the position of passage 5 is increased by 1, then difficulties
of the items linked to passage 5 would increase by .08 logit. Using the estimated passage
position effect, the adjusted difficulty of each item linked to passage 5 can be computed.
For example, the first item associated with passage 5 has a difficulty of −2.228. For the
Table 3 Descriptive statistics of latent trait estimates from the Rasch model across four
test forms
Table 4 Summary of estimated passage position effects
students who received passage 5 in the first position on the test, the estimated difficulty
of the item becomes −2.228 + (1 ∗ .08) = −2.148. If the same passage is administered in
the fifth position, the difficulty of the same item becomes −2.228 + (5 ∗ .08) = −1.828.
Using the estimated position effects in Table 4, the impact of changes in passage
positions on item difficulty can be calculated for other items and passages in a similar
fashion.
Table 5 shows the estimated item positions effects from M4. The results indicated that 23
out of the 45 items showed significant item position effects. The regression coefficients
for item position effects ranged from −.009 to −.037. All of the significant position
effects were negative. This finding suggests that as the positions of the items increase
(e.g., the item moves from the 1st position to the 15th position), the difficulties of the
items also increase and thus the probability of correctly answering the items decrease.
The largest item position effect was −.037 for item 26, which had an estimated difficulty
Table 5 Summary of estimated item position effects
of −3.244. If item 26 was administered in the first position on the test, then the
estimated difficulty of the item would become −3.244 + (1 ∗ .037) = −3.207. If, however,
the same item was administered at the 45th position (i.e., the last question) on the test,
then the estimated difficulty of the item would become −3.244 + (45 ∗ .037) = −1.579.
This finding suggests that changes in the position of this item could substantially
increase or decrease the probability of answering the item correctly. Adjusted
difficulties of other items with regard to their positions on the test can be calculated in the same
way.
Model comparison
Table 6 shows model fit information from the SEM models. In addition to the four
models described earlier, there was a variant of M4 that only included the significant item
position effects in M4. Using the Chi square difference test described in Eqs. 8 and 9, the
SEM models with either passage position or item position effects (M3 and M4) indicated
significantly better modeldata fit than the Rasch model (M1), whereas the SEM model
with form effects (M2) was not better than the Rasch model in terms of modeldata fit.
This finding is not surprising given the nonsignificant form effects in M2. Among the
four models that included additional predictors, M2 with form effects indicated the
worst model fit, whereas M4 with only significant item position effects indicated the best
model fit based on both AIC and BIC.
Simulation study
The empirical study demonstrated that the SEM approach is capable of detecting
different types of position effects in largescale assessments. In the second part of this study,
the Monte Carlo simulation capabilities of Mplus (Muthén and Muthén 1998–2015)
were used to investigate the extent to which the proposed SEM approach can detect
item position effects accurately. More specifically, the recovery of the SEM model in the
presence of a linear position effect on item difficulty was evaluated. Hit rates in detecting
items with the linear position effect and Type I error rates in flagging items with no
position effects were examined via simulated data sets.
Simulation design
To model the item characteristics of a largescale assessment, item difficulty
parameters obtained from the Rasch model in the empirical study were used as the population
Table 6 Summary of the model fit information from the SEM models
Number of parameters Loglikelihood value Scaling factor AIC
M1: Rasch model 45 −257,591
M2: Form effect 48 −257,590
M3: Passage position 52 −257,571**
effect
M4: Item position effect 90 −257,420**
M4: Item position effect* 68 −257,437**
* This model only included the significant item position effects in M4
** The model fits the data significantly better than M1 at the alpha level of α = .05
parameters when generating item responses in the simulation study. The item difficulty
parameters ranged from −2.894 to 0.196 (M = −1.22, SD = 0.63). Examinees’ latent
traits were drawn from a standard normal distribution, η ∼ N (0, 1). Two test forms with
45 dichotomous items were created. Six items were manipulated to have linear
position effects and the difficulty of the remaining items were not influenced by position
changes. Table 7 shows how item difficulty parameters of the six items were manipulated
as a result of their positions across the two test forms. There was no correlation between
item positions and the latent trait because item positions were randomly determined
independent of the latent trait.
The simulation study consisted of three factors: (a) the magnitude of the linear
position effect on item difficulty (.01 and .02 per one position change); (b) sample size (1000,
5000, and 10,000 examinees); and (c) the size of position change for the six manipulated
items across two forms (+10, +25, and +40 positions). The chosen values for the linear
position effect were similar to those found in the empirical study as well as the position
effects reported in earlier studies (e.g., Debeer and Janssen 2013). Similarly, sample size
values resemble the number of examinees from previous empirical studies on item
position effects (e.g., Bulut et al. 2016; Debeer and Janssen 2013; Qian 2014; Weirich et al.
2016). For each crossed condition, 1000 data sets were generated.
Model estimation
Each simulated data set was analyzed in Mplus using the SEM model in Eq. 7. Hit rates
(i.e., correctly detecting items with a linear position effect) and Type I error rates (i.e.,
falsely flagging items with no position effects) were evaluated. Furthermore, recovery
of item difficulty parameters was studied descriptively with root mean squared error
(RMSE) and mean error (ME) as follows:
k=1
1
ME = K
k=1
in=1(bni − bˆ i)2 ,
Table 7 Summary of the items with linear position effects in the simulation study
where bi is the true value of item difficulty of item i, bˆ i is the estimated value of item
difficulty for item i, and n is the total number of items (i.e., 45), and K is the number of
replications (i.e., 1000).
Table 8 shows the results of the simulation study. The 95% coverage indicates the
proportion of replications for which the 95% confidence interval contains the population value of
the linear position effect. Hit rate is the proportion of replications for which the items with
position effects were correctly flagged for exhibiting a linear position effect at the α = .05
level. Type I error rate is the average proportion of replications for which the items with
no position effects were falsely flagged for exhibiting a linear position effect at the α = .05
level. For every crossed condition, the estimated values of the position effect were very
similar to the population values of the position effect. Furthermore, the 95% coverage
column in Table 8 shows that the 95% confidence intervals of the estimated position effects
covered the population values of the position effects 94% or more of the time. These
findings suggest that the SEM model could successfully recover the position effect parameters,
even when the size of the position effect was small and sample size was not large.
Hit rates appeared to vary depending upon sample size, the magnitude of the
position effect, and the size of position change between two forms. When the magnitude
of the position effect was larger (β = −.02 per one position change), hit rates were
very high, except for the condition where sample size was 1000 and the size of position
change between two forms was 10. Hit rates improved as sample size, the magnitude
Change Total posi βˆ
of the magnitude of the position effect, and the size of position change between two
forms increased. Hit rates for the condition in which the magnitude of the position effect
was −.01 per one position change and the size of position change was 10 remained low,
despite increasing sample size from 1000 to 10,000. The average Type I error rates for
the items with no position effects were near the nominal rate (α = .05), which indicates
that the SEM model did not falsely flag items for exhibiting position effects. The RMSE
and ME values for the estimates of item difficulty were quite small, suggesting that the
recovery of the item difficulty parameters was good. The size of the RMSE and ME
values decreased even further as sample size increased.
Discussion and conclusions
Item position effect, which often is viewed as a context effect in assessments (Brennan
1992; Weirich et al. 2016), occurs when the difficulty or discrimination level of a test
item varies depending on the location of the item on the test form. For example, the
difficulty of an item can increase in later positions due to a fatigue effect or
decreasing testtaking effort (Hohensinn et al. 2011; Weirich et al. 2016). To investigate item
position effects, researchers have proposed different approaches using logistic
regression (e.g., Davey and Lee 2011; Pomplun and Ritchie 2004), multilevel IRT models based
on the GLMM framework (e.g., Albano 2013; Li et al. 2012; Weirich et al. 2014), and test
equating (e.g., Pommerich and Harris 2003; Meyers et al. 2009; Store 2013). The purpose
of the current study was to introduce a factor analytic approach for modeling item
position effects using the SEM framework. In the first part of the study, the methodological
capabilities of the proposed SEM approach were illustrated in an empirical study using
data from an operational testing program in reading. Test form, passage position, and
item position effects were investigated. In the second part of the study, a Monte Carlo
simulation study was conducted to evaluate the accuracy of the SEM approach in
detecting item position effects. The simulation study showed that the SEM approach is quite
accurate in detecting linear item position effects, except for the conditions in which both
the number of examinees and the magnitude of the item position effect are small.
The proposed SEM approach contributes to the literature of item position effects in
largescale assessments in three ways. First, the SEM approach allows researchers and
practitioners to examine both linear position effects and interaction effects in the same
model. It is typically assumed that item difficulty linearly increases or decreases as the
items are administered in later positions. However, changing item positions can also result
in changes in item difficulty as a result of the interaction of item positions and the latent
trait (e.g., Debeer and Janssen 2013; Weirich et al. 2014). Hence, it is important to evaluate
both linear position effects and interaction effects when designing a largescale assessment
containing multiple forms with different item orders or with randomized item ordering.
Second, the SEM approach presented in this study is a flexible method for studying
position effects with various IRT models for dichotomously and polytomously scored items—
such as the twoparameter model, Partial Credit Model, and Graded Response Model. For
example, the position analyses in the empirical part of this study could be easily extended
to the twoparameter IRT model by freely estimating factor loadings of the items (see
Fig. 2). Third, the SEM approach is applicable to largescale assessments with more
complex designs, such as multiple test forms (or booklets) consisting of the same set of items in
different positions, test forms with completely randomized item ordering for each
examinee, and multiple matrix booklet designs (Gonzalez and Rutkowski 2010).
Significance and future research
The current study has important implications in terms of educational testing practices.
First, this study evaluated position effects in a largescale assessment. The proposed
SEM approach can help practitioners identify problematic test items with significant
position effects and thereby leading to largescale assessments with improved test
fairness. Second, this study presents a straightforward and efficient approach to investigate
different types of position effects (e.g., item position effect, passage position effect, and
form effect). Hence, the proposed approach can be easily applied to assessments with a
large number of items and examinees. Third, the results of this study can provide
guidance for further research on position effects in computerbased and computerized
adaptive tests. For example, future research can focus identifying which types of items are
more likely to exhibit position effects in computerbased assessments and
computerized adaptive tests. This can help practitioners select the most appropriate items when
designing computerbased assessments and computerized adaptive tests.
This study introduced the SEM model that incorporates interaction effects, but did
not investigate the statistical properties of the proposed model. Thus, further research is
needed to evaluate the adequacy and accuracy of the proposed SEM model in detecting
interaction effects. Given the increasing popularity of multidimensional IRT models, it
would also be worthwhile to evaluate position effects in largescale assessments that
measure multiple latent traits. Finally, as Debeer and Janssen (2013) pointed out, there is a lack
of research on the underlying reasons of item position effects in largescale assessments.
Future research with the SEM approach can include itemrelated predictors (e.g., cognitive
demand, linguistic complexity) and examineerelated predictors (e.g., gender, test
motivation, anxiety) to explain why item position effects occur in largescale assessments.
Authors’ contributions
OB and MJG developed the theoretical framework for the analysis of position effects using structural equation modeling.
Furthermore, OB and QG carried out simulation and real data analyses for the study. All authors read and approved the
final manuscript.
Competing interests
The authors declare that they have no competing interests.
Appendix 1: Mplus code for testing passage position effects
Appendix 2: Mplus code for testing item position effects
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