MIS Research and Practice: Data Analysis with SEM Techniques – Part I

On August 21, 2010, one reader commented on

structural equation modeling (SEM) techniques as follow: “I don’t

really know enough about SEM, I know some, but I definitely think I

need to know more. Where is the best place to start for someone who

has a basic understanding, but wants to learn more?”

Dear

reader, the purpose of this post is to provide relevant information

on Structural Equation Modeling (SEM) to you. I also want to use this

opportunity to emphasize that SEM and other second generation data

analysis techniques are increasingly being applied in management

information systems (MIS) research and practice. I will be posting

additional materials on the use, usefulness, and ease of use of SEM

in MIS research and practice on my weblog.

SEM techniques are

important for MIS research and practice because they provide powerful

ways to address key IT research and practice problems such as

understanding IT usage, end-users satisfaction, IT strategic

alignment, IT project planning and implementation, knowledge

management projects, and so on. These techniques can be extremely

useful to IT executives who have IT substantive knowledge for

modeling, exploration, and interpretation of results.

Structural Equation Modeling

Structural

equation modeling (SEM) techniques refer to a range of multivariate

methods aimed at examining the underlying relationships (or

structure) among multiple predictor and criterion variables in a

model. A structural equation modeling process requires two steps:

(a) building and testing a measurement model, and (b) building and

testing a structural model. The measurement model serves to create a

structural model including paths representing the hypothesized

associations among the research constructs.

Various scholars

compared and contrasted SEM techniques to other multivariate

techniques such as multiple regression analysis, multivariate

analysis of variance (MANOVA), and canonical correlation analysis

(CCA). While multiple regression analysis assesses the relationship

between several independent (or predictor) variables and a dependent

(or criterion) variable, MANOVA assesses the relationship between two

or more dependent variables and classificatory variables or factors.

In contrast, SEM techniques represent only a single relationship

between the dependent and independent variables.

Multiple

regression and MANOVA do not consider the association effects between

criterion and predictor variables that generally characterized human

and behavioral problems in management. These techniques are unable

to examine a series of dependence associations concurrently. In

contrast, SEM techniques can expand the statistical efficiency and

explanatory ability for model testing with a single comprehensive

framework. According to various scholars, SEM techniques have the

ability to (a) model relationships among multiple predictor and

criterion variables, (b) represent latent variables (or unobserved

concepts) in those relationships, and (c) account for measurement

error in the estimation process.

Dow et al. (2008) compared

three structural equation techniques: path analysis, item level, and

parcel. Dow and his colleagues noted that these techniques produced

similar structural results even though they differed in the degree

they fit the data and the degree of explained variance. Path

analysis is considered as an extension of the regression model with a

regression performed for each variable in the model as endogenous

variable on others indicated by the model as causal. Item level SEM

uses individual items as measured indicators for latent variables and

requires a large number of parameters to estimate the fit of the

model to the data. Unlike path analysis, this method can test

hypotheses regarding the structural (path) model and the measurement

relations. Parcel SEM used item parcels to conduct analyses with

latent variables. Partial sum of responses to individual items were

derived from the parcels derived from partial sum of responses to

individual items. Our research study, for example, used path analysis

to build and test a business-IT strategic model for multinational

corporations.

Various vendors propose different SEM software

packages: Amos 5, EQS 6, LISREL 8, Mplus 3, PLS Graph 3, PROC CALIS,

and Mx. For our study, LISREL 8 was used for statistical analyses.

LISREL is a multivariate analytical software package intended for

standard and multilevel structural equation modeling techniques.

These methods accommodate different data types including complex

survey data on continuous variables and simple random sample data on

ordinal and continuous variables.

One can note an increased

use of structural equation modeling (SEM) techniques in the social

sciences and in information systems research. Prior research used

these techniques to address business-IT strategic alignment. Kearns

and Lederer (2003) used SEM to build and test a model that examined

the relationship between business-IT strategic alignment and

organizational strategies. Their research model contained 6

constructs, 26 items, general demographics, and 8 hypotheses. Kearns

and Sabherwal (2007) used SEM to create a structural model exploring

the linkage between IT strategic alignment and IT business value.

Their research model contained 9 constructs, 44 items, and 10

hypotheses.

Earlier studies using SEM did not propose models

of business-IT strategic alignment for multinational corporations

(MNCs). Our research study (Nkoyock, Spiker, Schmidt, & Martin, 2010)

used SEM to analyze data collected through the stratified random

sampling of two groups of IT and business managers. Our study drew

upon SEM techniques to build and test a business-IT alignment model

for MNCs with 10 constructs, 53 items, and 11 hypotheses.

SEM Modeling Process

SEM modeling process

is an incremental approach specifying the procedures for testing a

model. SEM analyses require essentially a 2-step modeling process of

building and testing measurement and structural models: (a)

measurement model, and (b) structural model.

The measurement

model serves to test the reliability and validity of the measures

based on the research model. This model postulates the relationship

between the measured items and the underlying constructs or factors.

The achievement of “best fitting” measurement model consists of

attaining a non-significant chi-square statistic and the recommended

values of other goodness-of-fit indices. The second step of the SEM

modeling process is the test of the structural model.
Our

research study used the measurement model to create a structural

model, including paths representing the hypothesized associations

among research constructs.

The structural model is the path

model defining the relationships among the latent or unobserved

variables. The structural model specifies which latent variables

cause changes in the values of other latent variables. Various

scholars posited the achievement of “best fitting” structural model

requires the incorporation of theory, substantive knowledge, previous

experience, or other guidelines to discern which independent

variables predict each dependent variable.

In our research

study, the initial structural model included three latent exogenous

variables (organizational emphasis on knowledge management,

management of perceived environmental uncertainty, and management of

transnational IS strategies), seven latent endogenous variables

represented the remaining constructs, and the hypothesized direct

paths. The main outcome of our study is a theoretical and practical

perspective of business-IT strategic alignment for multinational

corporations. For more details about the results of this research

study, please visit www.nkoyock.net.

Some

References:

Dow, K. E., Jackson, C., Wong, J., &

Leitch, R. A. (2008). A comparison of structural equation modeling

approaches: The case of user acceptance of information systems.

Journal of Computer Information Systems, 48(4), 106-114.

Kearns, G. S., & Lederer, A. L. (2003). A resource-based view of

strategic IT alignment: How knowledge sharing creates competitive

advantage. Decision Sciences, 34(1), 1-29. doi:

10.1111/1540-5915.02289
Kearns, G. S., & Sabherwal, R. (2007).

Strategic alignment between business and information technology: A

knowledge-based view of behaviors, outcomes, and consequences.

Journal of Management Information Systems, 23(3), 129-162.

Nkoyock, A., Spiker B. K., Schmidt T., & Martin, C. (2010).

Business-IT strategic alignment for complex multinational

corporations: The case of the U.N. Secretariat. MIS

Quarterly, to be published soon.