confirmatory factor analysis of transfer student adjustment

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Confirmatory Factor Analysis of TransferStudent AdjustmentJacob T. N. Young a & Elizabeth Litzler ba School of Criminology and Criminal Justice , Arizona StateUniversity , Phoenix , Arizona , USAb Center for Workforce Development , University of Washington ,Seattle , Washington , USAPublished online: 20 Sep 2013.

To cite this article: Jacob T. N. Young & Elizabeth Litzler (2013) Confirmatory Factor Analysis ofTransfer Student Adjustment, Community College Journal of Research and Practice, 37:11, 877-891,DOI: 10.1080/10668926.2010.515514

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Confirmatory Factor Analysis of TransferStudent Adjustment

Jacob T. N. Young

School of Criminology and Criminal Justice, Arizona State University,Phoenix, Arizona, USA

Elizabeth Litzler

Center for Workforce Development, University of Washington, Seattle, Washington, USA

Transfer students’ adjustment to college has received substantial attention by researchers. This focus

has predominately investigated the observation of transfer shock: a decrease in grade point average

(GPA) experienced after transferring. In response to the persistent focus on transfer shock, growing

attention has been directed toward other indicators of college adjustment suggesting that students

who transfer may experience adjustment difficulties in domains other than academics (e.g., social

and psychological). In addition, studies indicate that student experience in college differs by gender

and race, and there are increased calls to determine the factors that differentiate these groups.

However, no studies have validated whether different types of college adjustment are analytically

distinct constructs and whether they differ by demographic group. This article uses confirmatory

factor analysis to examine the factorial structure and measurement invariance of items from the

Laanan-Transfer Student Questionnaire (Laanan 2004), an inventory designed to measure the

multiple facets of transfer student adjustment. We use data from 1,079 engineering transfer students

from the Project to Assess Climate in Engineering (PACE) survey. The overall findings support the

factorial structure of adjustment being divided into academic, social, and psychological dimensions

and show that these measures are invariant across sex, race=ethnicity, and transfer institution type.

The findings from this study are important for researchers using such constructs in research studies

and for developing programs that specifically target the various domains of adjustment.

Both the number of community colleges nationwide and student enrollment at community

colleges has been growing over the past half century (NCES, 2008). In addition, community

colleges enroll greater proportions of minority, nontraditional and low-income students than

four-year institutions enroll, and 67% of students who immediately enroll in community

college after high school intend to eventually complete a bachelor’s degree (NCES, 2008).

With a growing need for a trained, domestic, technical workforce, students who begin

their postsecondary education at a community college will play an increasing role in filling

these workforce gaps. These different trends underscore the importance of understanding

community college students and their experiences transferring to four-year colleges and

universities because of the need to keep diverse students in technical baccalaureate degree

Address correspondence to Elizabeth Litzler, Center for Workforce Development, University of Washington, Seattle,

WA 98195-2135. E-mail: [email protected]

Community College Journal of Research and Practice, 37: 877–891, 2013

Copyright # Taylor & Francis Group, LLC

ISSN: 1066-8926 print=1521-0413 online

DOI: 10.1080/10668926.2010.515514

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programs. Valid and reliable measurement of transfer student experiences is necessary to

ensure research applies broadly to all groups.

The adjustment of students who transfer to a new institution has received substantial attention

from educational researchers (for reviews see Hills, 1965; Diaz, 1992; Laanan, 2001). These

studies overwhelmingly focus on changes to GPA, showing that transfer students experience

a decline in GPA upon transferring (referred to as transfer shock). And some studies indicate

that a large proportion of students eventually recover from this decline (see Diaz, 1992). Because

these studies predominately direct their attention toward GPA, less is known about other adjust-

ment difficulties transfer students face. In response to this empirical gap, recent attention has

been directed toward exploring other indicators of adjustment for transfer students (e.g., Berger

& Malaney, 2003; Laanan, 2004, 2007). In addition, attention has been focused on understand-

ing the degree to which factors such as student involvement (Astin, 1984); integration

(Tinto, 1975); student satisfaction (Bean, 1980); and quality of effort (Pace, 1980) affect transfer

student adjustment. Overall, a fruitful literature on the multiple dimensions of adjustment and

their various predictors is emerging.

The greatest attention toward this issue has been from Laanan (2004, 2007). Laanan’s (1998,

2004) Transfer Student Questionnaire (hereafter L-TSQ) is specifically designed to measure

multiple dimensions of adjustment to college (academic, social, and psychological adjustment)

and the factors that influence transfer student adjustment. Several studies have employed this

instrument to investigate the adjustment process (e.g., Laanan & Starobin, 2004; Laanan, 2007).

Undergraduate engineering transfer students are a unique group because their first classes in

college are highly technical, and depending on the existing articulation agreements, they may have

to retake certain required classes. Engineering lacks gender and racial diversity, and many research-

ers and policymakers are calling for more research on the reasons for underrepresentation of women

and minority students. While some research clearly identifies barriers for students in engineering, it

is becoming evident that women and underrepresented minorities have different experiences and

barriers in engineering (Litzler, Jaros, Brainard, & Metz, 2010). Prior scholarship indicates that

peoples’ experiences are very much informed by the intersection of race and gender (hooks, 1981,

Spelman, 1988). This suggests that female and minority transfer students, especially those entering

four-year engineering schools, may have different transfer experiences than the majority students.

Despite the attention to various domains of transfer student adjustment, no study to our

knowledge has confirmed the measurement properties of the L-TSQ items. Studies using the

L-TSQ items have used exploratory factor analysis (EFA) to develop variables measuring adjust-

ment. While EFA is an appropriate technique for exploring latent factors, it remains an open

question whether the properties of the various factors used to measure adjustment are confirmed.

The first purpose of this study is to validate items used to measure the various dimensions of

transfer student adjustment. We seek to achieve this goal by confirming the validity of items

measuring distinct adjustment constructs (i.e., academic, social, psychological). Although

Laanan (2004) develops the theoretical basis for the L-TSQ items and uses exploratory factor

analysis to investigate the interrelationships among the items, no study has confirmed the

measurement properties of these items. While exploratory factor analysis is useful for summar-

izing data, confirmatory factor analysis is the proper approach for confirming the measurement

properties of items hypothesized to measure underlying theoretical constructs. Therefore, testing

restrictions on, and establishing the factor structure of, transfer student adjustment is important if

researchers are going to use the L-TSQ items in future studies.

878 J. T. N. YOUNG AND E. LITZLER

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In addition, it remains unclear whether the measurement properties of these items are

invariant across demographic groups. A second purpose of this study is to determine whether

the validity of items measuring adjustment to college differs across demographic groups. If

the measures of latent constructs differ across groups, then the ability of the model to explain

differences within groups may vary across groups. In other words, the model of transfer student

adjustment may be more informative for one group than another. Evaluating cross-group

comparisons is important because studies indicate that the college experiences of underrepre-

sented groups may be very different (Litzler et al., 2010). Moreover, experiences may be

especially disparate in fields that are predominately populated by certain racial or gender groups

(e.g., undergraduate engineering students are overwhelmingly White and male). If these mea-

sures are invariant across groups, more confidence can be placed in assessing their ability to

explain transfer outcomes. However, if the items do differ across groups, the validity and utility

of the measures may be drawn into question. Moreover, because little attention has been directed

to understanding gender and race=ethnicity differences with respect to adjustment, confirming

the validity of these measures across these groups is of paramount importance for informing

future research.

For decades, research on transfer student adjustment was centered on the observation of trans-

fer shock (Hills, 1965). Multiple studies indicate that transfer students experience a dip in their

GPA when transferring (see Hills, 1965 for a review and Diaz, 1992 for a meta-analysis) and that

they tend to recover from this decline (Holahan, Green, & Kelley, 1983), although the relation-

ship may not be robust across majors (e.g., Cejda, Kaylor, & Rewey, 1998). Despite the near

exclusive focus on academic adjustment, attention has more recently focused on social and

psychological adjustments that occur during this transitional period (Laanan, 2001). The most

comprehensive approach to measuring transfer student adjustment is the Laanan-Transfer

Student Questionnaire (L-TSQ), which was designed to provide a more developed understanding

of difficulties that transfer students experience when they transfer to a new institution

(Laanan, 1998, 2004).

Laanan (2004, p. 341) conceptualizes the adjustment to a novel setting as composed of three

factors: psychological, academic, and social. These multiple dimensions reflect the complex

adjustment process faced by transfer students and are designed to capture not simply academic

changes, but also the cultural changes experienced by students (Pascarella & Terenzini, 1991).

Laanan used EFA to develop these factors from L-TSQ items and show that the factors are valid

and reliable (2004, 2007). However, these factors have been created through a data reduction

technique (i.e., EFA). As a result, the factor structure developed in these studies has not been

confirmed and the equivalence of the items across demographic groups has not been demon-

strated. To fill this gap in the literature, we use confirmatory factor analysis (CFA) to confirm

the factors constructed by Laanan (2007) as recent work has shown that this approach can be

important for future research (e.g., Marti, 2009).

Women and certain minority groups are highly underrepresented in undergraduate engineer-

ing programs, constituting about 18% and 12% of students, respectively (Commission on Profes-

sionals in Science and Technology, 2008; Gibbons, 2008; National Action Council for

Minorities in Engineering, 2008). Research on engineering students shows important experien-

tial differences between women and men and between students of different races (Litzler et al.,

2010). According to the report ‘‘Women and Men of the Engineering Path,’’ women and men

earn similar grades in engineering courses, but only 42% of women complete their degrees

FACTOR ANALYSIS OF TRANSFER STUDENT ADJUSTMENT 879

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compared with 62% of men (Adelman, 1998). Prior research documents that women indicate

less comfort using lab equipment, less comfort participating in class, less confidence in their

engineering success, less enjoyment of their classes, a greater sense of engineering community,

a greater sense that students help each other succeed in class, than men indicate (Metz, Brainard,

& Gilmore, 1999; Grandy, 1994; Litzler, Jaros, Brainard, & Metz, 2010; Sax, 1996). In one of

the few studies looking at differences between engineering students of different race=ethnicities,the researchers find that African American and Hispanic American engineering students do not

feel that they are taken seriously by their peers compared to White students. In addition, Hispanic

American students are less likely than White students to indicate that they are comfortable asking

questions in class (Litzler et al., 2010). These studies indicate that the college experience for

engineering transfer students varies based on race=ethnicity and gender. As a result, it is impor-

tant to determine whether observed differences between these groups on the L-TSQ items are

the result of differences in the underlying factor structure of student adjustment or genuine

differences in adjustment to college.

METHODS

Instrumentation

For this study, we use data on 1,079 engineering transfer students from 21 schools who

participated in the Project to Assess Climate in Engineering (PACE) survey, which is funded

by the Alfred P. Sloan Foundation. The PACE survey was designed to measure the engineering

educational climate at universities in the United States. To increase comparability across the

research sites, the PACE study was restricted to those undergraduate engineering programs

defined as one-tiered. In other words, each of the programs either enrolls its students directly

from high school into the college=school of engineering and=or provides an engineering advisor

to students during the first year who indicated an interest in engineering on their college appli-

cation form. A stratified random sample of undergraduate engineering students with oversamples

of females and underrepresented minorities (URMs) created the survey population. Between

February and June of 2008, 38,376 engineering undergraduate students were invited to partici-

pate in the PACE online climate survey and 10,554 students responded. The response rate at

individual institutions ranged from 7% to 52% with an overall mean of 29% and a median of

28%. Of the 10,554 students who responded, 1,079 indicated that they had transferred to the

university from a domestic educational institution. The PACE instrument took respondents

approximately 15 minutes to complete 132 items. Questions covered many areas including

professor and student interaction, extracurricular activities, and confidence. Transfer students

were asked to complete an additional set of 24 questions that were drawn from the L-TSQ

inventory (Laanan, 2004).

Variables

To measure the multiple dimensions of adjustment to college, we explore psychological,

academic, and social adjustment following Laanan (2004). The variables are chosen based on

the exploratory factor analysis (EFA) results reported by Laanan and the items available in


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the PACE survey. The following are the indicators for psychological adjustment: (a) feel

overwhelmed by size of student body; (b) the large classes intimidate me; (c) it is difficult to

find my way around campus; and (d) upon transferring I felt alienated at this university. Indica-

tors for academic adjustment are the following: (a) adjusting to academic standards or expecta-

tions has been difficult; (b) I experienced a dip in grades during first and second

quarter=semester; and (c) my level of stress increased when I started at this university. Indicators

for social adjustment are these: (a) I am meeting as many people as I would like; (b) it is easy to

make friends at this four-year university; (c) I am very involved with social activities at this

four-year university; and (d) adjustment to the social environment has been difficult. The

response categories for all items were on a five-point Likert scale ranging from Strongly Agreeto Strongly Disagree. All variables were standardized into z-scores prior to analysis.

For cross-group comparisons we focus on gender (males and females); race=ethnicity(Whites, African Americans, Native Americans, Hispanics, and Asians); and transfer institution

type (two-year or four-year institution). The PACE survey data are unique because of the large

number of underrepresented minorities and transfer students who answered the survey. This

enabled analysis by racial and ethnic group, which is not common in engineering studies.

The data answer the continued call for analyses that do not combine all URMs into one group.

Instead, these analyses focus on the experiences of each racial and ethnic group, as well as

disaggregating by gender and race=ethnicity (George, Neale, Van Horne, & Malcom, 2001;

Leggon, 2006; Ong, 2005). For this analysis, White students comprised the largest racial group

(62.1%) in the data, followed by Hispanic (23.7%), Asian-American (6.9%), African-American

(4%), and Native American (3.1%). Males made up the largest group based on gender (64%),

and two-year institution transfers were the slight majority (55%) of all transfer students. These

figures are representative of enrollment figures for engineering majors. Descriptive statistics for

the variables are given in Table 1. Difference of means tests for each item by group are provided

in the Appendix.

TABLE 1

Descriptive Statistics

Validobservations Mean

Standarddeviation Min Max

Psychological AdjustmentI often feel overwhelmed by size of student body 1059 2.121 1.127 1 5

The large classes intimidate me 1062 2.063 1.111 1 5

It is difficult to find my way around campus 1059 1.881 1.047 1 5

Upon transferring I felt alienated at this university 1058 2.224 1.245 1 5

Academic Adjustment

Adjusting to academic standards or expectations has been difficult 1053 2.561 1.331 1 5

I experienced a dip in grades during first and second quarter=semester 1057 2.989 1.502 1 5

My level of stress increased when I started at this university 1058 3.473 1.351 1 5

Social Adjustment

I am meeting as many people as I would like 1056 3.437 1.201 1 5

It is easy to make friends at this four-year university 1048 3.476 1.151 1 5

I am very involved with social activities at this four-year university 1048 2.712 1.238 1 5

Adjustment to the social environment has been difficult 1055 2.491 1.282 1 5


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Analytic Approach

The analysis is divided into two parts. First, we estimate a series of CFA measurement models

to test the factorial structure of adjustment to college. These models investigate whether

psychological, academic, and social adjustment are analytically distinct factors among

undergraduate engineering students. Second, we evaluate the invariance of the measures across

three demographic groups: gender, race=ethnicity, and transfer institution type. For each group-

ing, a series of models are compared in which constraints on the model are relaxed and the fit is

evaluated. All models are estimated using EQS 6.1. To evaluate models in the factor structure

analysis, we use the x2 test, root mean squared error of approximation (RMSEA) (Steiger,

1990); the standardized root mean residual (SRMR); and the comparative fit index (CFI)

(Bentler, 1990). These fit measures are the most commonly used and most recommended.

Several other measures of model fit were used including the Aikaike’s information criteria;

goodness-of-fit index (Hair, Anderson, Tatham, & Black, 1998); and the nonnormed fit index

(Bentler & Bonett, 1980), which produced similar model fit and are excluded due to space con-

straints. To evaluate model fit when testing measurement invariance, we evaluate the x2 ratio thatdescribes the improvement in the fit of a model when specific parameter constraints are relaxed.

Model fit is described in more detail in the Findings section.

FINDINGS

Missing Data

Incomplete information is a common problem with survey data. Deleting cases with incom-

plete data removes important information and poses serious problems to statistical inference

(Graham, 2009). Alternatively, imputing the mean to cases that are missing data fails to take

into account the uncertainty in data collection (Hoff, 2009). To account for missing data, we

estimate missing values using normal theory maximum likelihood for 4% of the cases in our

sample. We employ the expectation maximization (EM) algorithm (Dempster, Laird, &

Rubin, 1977) for mean and covariance structure models proposed by Jamshidian and Bentler

(1999) that computes maximum likelihood estimates using EQS 6.1. This methodology

rebuilds the covariance matrix and the sample means estimates with the EM algorithm leading

to more accurate results compared to traditional missing data imputation methods (Peugh &

Enders, 2004). All parameters were estimated using maximum likelihood with robust

standard errors.

Factor Structure of Adjustment Items

A series of CFA measurement models were constructed with the L-TSQ items to test the theor-

etical construct Adjustment to College. Figures 1, 2, and 3 show the models tested. Following

the conventional illustration of factor models, the squares represent the indicators of the factors.

The arrows from the latent factor (the circle) to the square indicators are the k or factor loadings.

Finally, the error terms are represented by an ‘‘e’’ pointing to the corresponding indicator.

Figure 1 shows the Unidimensional Factor, First Order Model, which serves as a comparison


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model. This model assumes that the various measures of adjustment are indicative of a single

underlying construct. Figure 2 shows the Three Factor, First Order Model which breaks the

factors into the different types of adjustment: psychological, academic, and social. This model

FIGURE 1 Unidimensional factor, first order.

FIGURE 2 Three factor, first order.


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(Laanan, 2004) postulates that adjustment to college using these items is best conceptualized as

three distinct latent factors. Finally, Figure 3 shows the One Factor, Second Order Model, which

proposes that the relationship between the three first order latent factors (i.e., psychological, aca-

demic, and social) are related through a second-order latent construct. The difference between

the models in Figure 2 and Figure 3 is that the latter implies that the indicators for the latent

variable Adjustment are also latent (i.e., the indicators are the latent variables psychological, aca-

demic, and social adjustment).

Table 2 shows the standardized factor loadings for the adjustment items and the fit measures

for the corresponding models. Symbols denoting significance are excluded because all parameter

estimates were statistically significant at the p< .05 level. Compared with models 2 and 3,

model 1 shows very poor model fit. For example, the RMSEA is substantially greater than

0.10, the SRMR is greater than 0.90, and the CFI is substantially less than 0.90, all of which

are threshold values that indicate poor model fit (Williams, Fletcher, & Ronan, 2007). The poor

fit of model 1 indicates that the measures of adjustment to college capture distinct factors. Com-

paring models 2 and 3, the differences in the fit statistics for the models are less severe than the

differences observed in the comparison for either model with model 1. Overall, the model fit

measures indicate that model 2, the model with three separate factors for adjustment to college

and no second-order factor, provides the best fit to the data. Although psychological, academic,

and social adjustment to college are correlated, these factors are three distinct constructs, not

indicators for a higher-order factor. This finding confirms past studies that constructed separate

adjustment scales based on exploratory factor analyses (Laanan, 2004, 2007) to analyze the vari-

ous ways in which transfer students adjust to college. Moreover, the results reveal that, in

addition to other populations, the L-TSQ items are valid indicators for engineering students, a

population that has not been previously targeted for analysis with these measures.

FIGURE 3 Single factor, second order.


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Measurement Invariance

Although the previous section has confirmed the factor structure of adjustment items, it is important

to determine whether this structure differs across gender, race=ethnicity, or type of transfer

institution. To evaluate measurement invariance in the adjustment items across groups, we first

estimate a model in which no cross-group constraints are imposed on the parameters. Then, we com-

pare invariance of measures based on gender (males and females), race=ethnicity (Whites, African

Americans, Native Americans, Hispanics, and Asians), and transfer institution type (two-year or

four-year institution). Table 3 presents the results for a series of models based on these groups. For

each grouping (i.e., gender, race=ethnicity, transfer institution type), we estimate a model with free

parameters and then impose equality constraints in three steps. This progression allows us to deter-

mine whether the fit of the model improves as restrictions on the parameter estimates are relaxed. First,

constraints are placed on the factor loadings forcing the regression coefficients to be equal across the

groups (Lambdas Invariant). Next, we constrain the factor loadings and the variance for the measure-

ment errors to be equal across the groups (Lambdas and Measurement Error Variances Invariant).Finally, we constrain the factor loadings, the variance for the measurement errors, and the factor

covariances to be equal across the groups (Lambdas, Measurement Error Variances, and FactorCovariances Invariant). This third restriction tests whether there is equivalence in the structural modelacross the groups. That is, whether the covariances between the latent variables differ across groups.

TABLE 2

Standardized Factor Loadings for Adjustment Items and Model Fit Comparisons (N¼1,079)

Model 1:

Unidimensional

factor

Model 2:

Three factors,

first order

Model 3:

One factor,

second

Item

Factor 1: Psychological Adjustment

Feel overwhelmed by size of student body 0.694 0.792 0.855

Large classes intimidate me 0.701 0.806 0.831

Difficult to find my way around campus 0.561 0.601 0.616

Upon transferring felt alienated at this university 0.734 0.684 0.667

Factor 2: Academic Adjustment

Adjusting to academic standards has been difficult 0.641 0.829 0.827

Experienced a dip in grades during first and second term 0.452 0.672 0.676

Level of stress increased when I started at this university 0.501 0.674 0.679

Factor 3: Social Adjustment

Am meeting as many people as I would like 0.402 0.767 0.768

It is easy to make friends at this university 0.507 0.916 0.919

Am very involved with social activities at this university 0.176 0.382 0.387

Adjustment to social environment has been difficult 0.737 0.498 0.491

Fit Measures

Chi squared 2,003.475 465.547 604.765

Root mean squared error of approximation (RMSEA) 0.181 0.088 0.101

Standardized root mean residual (SRMR) 0.113 0.058 0.092

Comparative fit index (CFI) 0.662 0.926 0.902

Note. The three factors only apply to models 2 and 3; model 1 assumes that all items load onto a single factor. Indi-

cators of significance level excluded because all factor loadings are significantly different from zero at the p< .05 level.


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To evaluate the constraints, Table 3 presents the statistics for each model as well as the

ratio of the difference in x2 statistics between models to the difference in degrees of freedom

between models. This ratio (i.e., x2current model � x2free model=dfcurrent model � dffree model) describes

the improvement in the fit of a model when constraints are released. Large ratios indicate that

the constraints relaxed in the free model significantly improve fit over the current model. For

example, if a model where a single constraint is released (i.e., loss of one degree of freedom)

produces a corresponding ratio greater than 3.84 (the critical value for the x2 statistic at the

p< .05 level), then there is evidence that relaxing this constraint significantly improves the fit

of the model. Significant improvements in model fit inform us that there are important

cross-group differences in the measurement properties for the adjustment items.

As can be seen in Table 3, for all comparison types the results are identical. For example, in

the model comparisons based on gender, the x2 for the free model, or model 1.A, is 804 with 80

degrees of freedom, indicating that the model provides a good fit for the data. In model 1.B,

which constrains the lambdas to be invariant, the x2 statistic is 806 with 89 degrees of freedom.

The ratio of the difference in x2 statistics between model 1.B and 1.A to the difference in degrees

of freedom between model 1.B and 1.A is 2=9 or 0.22. This ratio indicates that model 1.B pro-

vides an adequate fit to the data and that the fit is not worse than the fit provided to the data by

model 1.A. In other words, freeing the constraints in model 1.B (which becomes model 1.A)

does not significantly improve the fit of the model to the data. The same results are observed

for models 1.C and 1.D. That is, the ratios indicate that the adjustment factors—as well as

the covariances between the factors—are equivalent for males and females. Identical results

TABLE 3

Assessing Invariance in the Adjustment Scales for Gender, Race=Ethnicity, and Transfer Institution Type:

Nested Measurement

Chi square

Degrees

of freedom

Chi-Square=

df ratio

Ratio

difference from

free modela

Gender (Model Set 1)

1.A Free model 804.153 80 10.051 —

1.B Lambdas invariant 806.911 89 9.066 0.221

1.C Lambdas and measurement error variances invariant 809.737 97 8.347 0.295

1.D Lambdas, measurement error variances, and factor

covariances invariant

810.067 100 8.101 0.301

Race=Ethnicity (Model Set 2)

2.A Free model 647.902 200 3.239 —





758.446 278 2.728 1.421

Transfer Institution Type (Model Set 3)3.A Free model 470.845 80 5.885 —





503.514 100 5.031 1.657

aChi squarecurrent model – Chi squarefree model)=(dfcurrent model – dffree model).


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are observed for race=ethnicity and transfer institution type. Overall then, Table 3 shows that the

measurement properties of the items measuring Adjustment to College are invariant across the

engineering transfer student groups investigated, providing strong evidence that the L-TSQ

items are an appropriate instrument for making comparisons between demographic groups.

CONCLUSION AND IMPLICATIONS FOR PRACTICE

While initial research on transfer students focused on academic adjustment to college in terms of

GPA changes, more recent research has examined other dimensions of transfer student adjust-

ment including social and psychological adjustment. The Laanan-Transfer Student Question-

naire (L-TSQ) was developed by Laanan (1998, 2004) to provide additional measures of

transfer student adjustment. Several studies have employed this instrument to investigate the

adjustment process (e.g., Laanan & Starobin, 2004; Laanan, 2007). However, no study has

confirmed the measurement properties of the L-TSQ items. We use data from a sample of under-

graduate engineering transfer students to test the factor structure and measurement invariance for

indicators of the various domains of adjustment by using confirmatory factor analysis.

There are three primary findings. First, the construct of Adjustment to College is indeed

composed of three separate factors: psychological, academic, and social adjustment. This validates

the three measures of transfer student adjustment as distinct constructs, each of which is important

to understand and analyze. When students enter a new academic environment, they may not feel

like they fit in a number of ways including psychologically, academically, and socially. Our

findings confirm the importance of evaluating the various ways in which students adjust to

college after transferring. Our study, however, does not rule out the possibility that there may

be additional components of adjustment. Because the three constructs of adjustment that we have

investigated are the primary components focused on in the literature, developing items for

additional constructs remains a task of future research.

The second finding is that the L-TSQ items are valid indicators of transfer student adjustment

for a subset of the undergraduate population (i.e., engineering students). Engineering is more

technical, time-intensive, and limiting in terms of class flexibility than many baccalaureate

majors. Consequently, engineering transfer students might be more likely to experience dif-

ficulty in adjusting to college, and, therefore, they constitute a unique transfer population. This

study did not ascertain whether engineering students experience higher levels of transfer shock

or other adjustment issues relative to other majors. However, we have determined that the three

adjustment factors operate similarly for engineering students compared to prior research using

EFA with a more general student population. The generalizability of our findings to other majors

remains an empirical question to be addressed by future research.

Lastly, the measurement properties of the transfer student adjustment items are similar across

demographic groups. The L-TSQ is equally good at measuring adjustment for each of the

demographic groups investigated: men and women, Whites, African Americans, Native

Americans, Hispanics and Asians, and students from two-year and four-year transfer institu-

tions. Our results indicate that, although these groups may experience college differently, the

model of adjustment proposed by Laanan (1998) does not differ for these groups. This finding

complements previous research documenting that men and women and minority and majority

students experience life and college in different ways. The earlier research does this by showing


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that differences in experience are not an artifact of differences in the factor structure of college

experience.

These findings suggest that the Laanan-Transfer Student Questionnaire (Laanan, 1998, 2004)

provides appropriate measures for evaluating the multifaceted ways in which students adjust to col-

lege. The results presented here reveal several important implications. First, the L-TSQ can be used

by community colleges and universities to understand the most important needs of their student

body and develop programs that target these dimensions of adjustment. Even if the L-TSQ is not

used to conduct needs assessment, educators and practitioners can design interventions that focus

on all three of the adjustment constructs, providing multifaceted support for transfer students. In

addition, program directors of interventions targeting transfer students can confidently use the

L-TSQ to evaluate the impact of their interventions across race, gender, and even type of transfer.

As with all research, there are limitations to our findings. First, we are unable to determine

whether the results apply to students from other majors as our sample is restricted to engineering

students. Without any theoretical claim for suspecting differences between majors in the factor

structure of adjustment, it is difficult to predict whether our findings will differ for nonengineer-

ing majors. A second limitation is that our data were limited to undergraduate engineering pro-

grams defined as one-tiered. These are programs that either enroll its students directly from high

school into the college=school of engineering and=or provide an engineering advisor to students

during the first year who indicated an interest in engineering on their college application form.

Doctoral-granting research universities are more likely to be two-tiered schools. Because

students do not enter a major until their second or third year at two-tiered universities, the

transfer experience may be different.

Engineering is a field that greatly needs more diversity, and community colleges could be an

important pipeline to move more women and minorities into the field. But this recruitment will

be nullified if the transfer adjustment process is so difficult that such students leave school or

move to a different major. Retaining women and minorities in engineering is necessary to accel-

erate innovation in engineering and technology, and transfer students will play an important role

in the growing global economy.

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APPENDIX

ComparisonofMeansandttests

forEqualityofMeans(N

¼1,079)

Gender

Race

TransferInstitutionType

Males

Fem

ales

White

Hispa

nic

African

American

Asian

American

Native

American

2-year

4-year

Psychological

Adjustment

Ioften

feel

overwhelmed

bysize

ofstudentbody

3.797

3.923a

3.935

4.086b

3.785

3.464d

4.121

3.857

3.938

Thelargeclassesintimidateme

3.763

4.034a

3.943

4.157b

4.047

3.563d

4.091

3.907

4.014

Itisdifficultto

findmyway

aroundcampus

4.021

4.168a

4.145

4.256

4.024

3.94

4.242

4.096

4.156

UpontransferringIfeltalienated

atthisuniversity

3.669

3.840a

3.889

4.003

3.619

3.478d

3.424e

3.724

3.876f

Academic

Adjustment

Adjustingto

academ

icstandardsorexpectations

has

beendifficult

3.359

3.482

3.519

3.537

3.166

3.157d

3.575

3.29

3.642f

Iexperiencedadip

ingrades

duringfirstand

secondquarter=semester

2.912

3.083a

3.06

2.920

2.857

2.80

3.281

2.793

3.343f

Mylevel

ofstress

increasedwhen

Istarted

atthisuniversity

2.416

2.594a

2.528

2.553

2.595

2.35

2.575

2.287

2.872f

Social

Adjustment

Iam

meetingas

manypeople

asIwould

like

3.445

3.425

3.507

3.543

3.166

3.46

3.333

3.423

3.438

Itiseasy

tomakefriendsat

thisfour-yearuniversity

3.382

3.520a

3.557

3.620

3.0714c

3.58

3.272

3.453

3.479

Iam

veryinvolved

withsocial

activitiesat

this

four-yearuniversity

2.775

2.660

2.726

2.591

2.707

2.86

2.606

2.537

2.908f

Adjustmentto

thesocial

environment

has

beendifficult

3.305

3.614a

3.613

3.746

3.333

3.183d

3.454

3.484

3.536

aMeanforfemales

issignificantlydifferentfrom

males

atthep�.05level.

bMeanforHispanic


Whiteat

thep�.05level.

c MeanforAfrican

American


Whiteat

thep�.05level.

dMeanforAsian

American


Whiteat

thep�.05level.

e MeanforNativeAmerican


Whiteat

thep�.05level.

f Meanfor4-yeartransfer


2-yearnatthep�.05level.

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confirmatory factor analysis of transfer student adjustment

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