ADVANCED STATISTICAL LECTURING SERVICES
WHAT IS SEM?
Structural Equation Modeling (SEM) — which is sometimes called LInear Structural Relationships (LISREL), Analysis of Moment Structures (AMOS), Latent Variable Analysis, Analysis of Covariance Structures, or Causal Modeling — is an umbrella term for a statistical technique that is used widely by researchers in a diverse array of fields to replace many conventional analytical tools such as regression, path analysis, factor analysis, canonical correlation, analysis of variance, analysis of covariance, principal component analysis, classical test theory, and non-recursive econometric modeling.
Essentially, SEM combines factor analysis with path analysis (including simple, multiple, or multivariate regression) to investigate complex relationships amongst observed, measured variables and unobserved, latent variables (or factors), as well as amongst the latent variables themselves. The following diagram demonstrates a typical SEM model:
WHAT IS MLM?
In conventional regression analysis, it is assumed that subjects are randomly selected — and that therefore all variance in the dependent variables is due solely to variation amongst individuals. However, in many studies, subjects are clustered within a group and multiple groups are sampled. For example, in an education study, we may have students clustered within classes and multiple classes sampled; in a human resourcing study, we may have employees clustered within work units and multiple work units sampled. In such sampling, although some of the variance in the dependent variables will be due to variation amongst individuals, some the variance in the dependent variables will also be due to variation amongst the groups themselves. In such cases, Multi-level Modeling (MLM) should be employed to account for the different levels of variation.
Repeated measure designs should also be analysed using MLM, because the repeated observations are nested within subjects. For example, in a marketing study, we may have repeated measures of consumers’ attitudes to a brand over the life of a marketing campaign; in an epidemiology study, we may have repeated measures of a health outcome over the life of a drug treatment regime. In such studies, although some of the variance in the dependent variables will be due to variation across the various time occasions, some the variance in the dependent variables will also be due to variation amongst individuals themselves. Again, in such cases, Multi-level Modeling (MLM) should be employed to account for the different levels of variation.
WHERE HAS PHILIP TAUGHT
Since 1994, Philip has taught more than 100 ACSPRI courses. In that time, he has enhanced the research capacity of over 1,500 PhD students, university researchers, and researchers from numerous research institutions and government departments. In addition, Philip has been engaged directly by numerous individual university faculties to conduct in-house intensive SEM and/or MLM courses. In that capacity, he has enhanced the research capacity of at least another 1,500 PhD students and university researchers.
In total, Philip has now taught in over half the universities in Australia including the Australian National University, Charles Sturt University, Curtin University of Technology, Deakin University, Edith Cowan University, Griffith University, La Trobe University, Macquarie University, Monash University, RMIT University, Swinburne University of Technology, the University of Adelaide, the University of Melbourne, the University of New England, the University of Newcastle, the University of Queensland, the University of Sydney, the University of Western Australia, University of South Australia, University of Technology Sydney, University of the Sunshine Coast, Victoria University, and Western Sydney University.
COURSES
The instructor, Philip Holmes-Smith, offers one Multi-level Modeling (MLM) course and a number of Structural Equation Modeling (SEM) course options. SEM courses are offered:
- at two levels — Introductory SEM or Advanced SEM;
- using one of two programs — SEM using AMOS or SEM using Mplu; and
- in two forms — courses offered at your institution or through ACSPRI.
The MLM using Mplus course is offered:
- in two forms — course offered at your institution or through ACSPRI.
INTRODUCTORY SEM or ADVANCED SEM COURSE?
Philip has broken the full breadth of SEM topics into seven main parts. Parts I–IV can be considered as the introductory topics to be covered in the Introduction to Structural Equation Modeling courses, whereas Parts V–VII can be considered as the advanced topics to be covered in the Advanced Structural Equation Modeling courses.
Introductory SEM Courses:
Part I provides a revision of the two components to SEM, namely factor analysis and regression, and introduces the course software (AMOS or Mplus). Part II covers the fundamental concepts underlying SEM and outlines the eight steps typically followed in conducting SEM. Part III give examples of three types of basic models. The first type includes univariate and multivariate regression models, recursive path analysis, and non-recursive econometric models. The common aspect to these models is that all variables are observed (i.e. there are no latent variables). The second type of models include factor analytic models. We begin by exploring issues surrounding one-factor congeneric measurement models. We then move onto multi-factor analytic models, including second-order factor models and multi-trait, multi-method models. Finally, we look at full structural equation models with latent variables including mediating variables. Part IV deals with more complex issues and extensions around the basic models covered in the introductory chapters, including data issues and problem models.
Advanced SEM courses:
Part V deals with advanced, single-level models including multi-group analysis, models with interactions, and mean structure analysis including analysis of covariance. In all these models, it is assumed that the data comes from a simple random sample of cases. On the other hand, Part VI deals with advanced, multi-level models where the data has been sampled hierarchically. This includes repeated-measure designs where measures are recorded for each individual on numerous occasions over time (i.e. repeated measures nested within individuals) or where the measures are collected from individuals who share a common group membership (i.e. individuals nested within groups). Finally, Part VII deals with mixture modeling. Mixture modeling refers to modeling where group membership is unknown but can be inferred from the data (i.e. group membership is a categorical latent variable).
SEM using AMOS or SEM using MPLUS?
The introductory and advanced SEM courses are offered using either the AMOS or Mplus software program. When deciding whether to do a course using AMOS or Mplus, consider the following:
Mplus (developed by Bengt Muthén) owes its popularity to a number of factors. First, Mplus is the only SEM program that can deal with dependent latent categorical outcomes (e.g. Did the subject survive or die? Was the student’s achievement above or below a required minimum standard? etc.). It is also the best program at handling a wide variety of data types (e.g. continuous, categorical, nominal, count, and censored data). Second, Mplus can estimate a broader range of models including multi-level regression analysis, multilevel growth modeling, survival analysis, and complex mixture modeling. Third, Mplus is very well supported, including the worldwide short courses conducted from time-to-time by Bengt Muthén and others; the short course videos and handouts provided on the Mplus website; and the excellent support desk provided through the Mplus website.
AMOS (developed by James Arbuckle) is extremely popular. It owes its popularity to two factors. Firstly, AMOS has been included as an "add-on" module to IBM SPSS, which means that in the many hundreds of universities around the world that have site licenses for IBM SPSS, students and staff can get access to AMOS without the need to purchase additional stand-alone software. Secondly, and probably even more importantly, AMOS is by far the easiest of the products to use because of its “user-friendly” graphical interface — there is no need to learn complex syntax. In fact, it is probably fair to say that the user-friendly interface of AMOS has made the teaching of structural equation modeling a possibility in far more statistics programs and has been a major factor in the increasing use of structural equation modeling in the literature.
AMOS and Mplus differ in several respects. Although AMOS can be used to run multilevel structural equation models, it lacks a pre-processor capable of partitioning the variance-covariance matrix into the various levels required to conduct such models; nor does it have the multi-level regression or multilevel SEM analysis capacity of Mplus. On the other hand, the way AMOS facilitates the checking of path moderation in multi-group analysis is far simpler than in Mplus. AMOS and Mplus also differ in the way they adjust the χ2 goodness-of-fit statistic and standard errors to compensate for non-normality in the data. Mplus implements the Satorra-Bentler adjusted χ2 and robust standard error (Satorra and Bentler; 1994); AMOS implements the Bollen-Stein adjusted p and bootstrapped standard error (Bollen and Stein; 1992).
MLM using MPLUS
The Multi-level Modeling course is offered using Mplus only (see the course description below).
SEM and MLM COURSES offered at YOUR INSTITUTION or through ACSPRI
Courses offered at your institution:
The instructor, Philip Holmes-Smith, offers on-site three-, four-, or five-day programs for university faculty staff and students or research organisation staff. A three-day program covers either the Introductory or Advanced SEM coursework or the MLM coursework. In addition to the three-day coursework, a further one or two days may be added, either to combine some course components and/or work with individuals or small teams on their specific research problems.
ACSPRI programs:
If only one or two individuals from your organisation are seeking training in SEM, probably the best option is to attend one of the five-day courses offered through ACSPRI. The Australian Consortium for Social and Political Research Incorporated (ACSPRI) is a not-for-profit consortium comprising most universities in Australia, many government departments, and several national research organisations. Its most prominent activities are its regular five-day training programs in social research methods and research technology. These annual summer and winter programs currently alternate through Canberra and Melbourne (summer) and Brisbane (winter). Philip Holmes-Smith developed the Applied (Introductory) and Advanced SEM courses and the Multi-level Analysis/Modeling course offered by ACSPRI and continues to present these ACSPRI courses himself. Information about ACSPRI courses can be viewed at www.acspri.org.au.
INTRODUCTION TO STRUCTURAL EQUATION MODELING (SEM) using AMOS
About the course:
The target audience for this course is post graduate students, academic staff, and other researchers needing to learn how to run basic structural equation models using the AMOS software. This course is designed as an introduction to Structural Equation Modeling and the use of AMOS to estimate such models.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
Course Syllabus:
Introduction, revision, and the fundamentals of SEM.
Topics include a revision of factor analysis and regression analysis and their relevance to SEM. Participants will be introduced to the AMOS program, including how to draw AMOS diagrams, how to run models, and how to review output. We will also cover the fundamentals of SEM. Topics include: the advantages of SEM over conventional analytical techniques, the fundamentals underlying SEM, and an overview of the eight basic steps to SEM.
The eight steps of SEM.
The eight steps of SEM are covered in detail, namely:
- Step 1 - model conceptualisation;
- Step 2 & 3 - path diagram construction and model specification using the AMOS graphical interface;
- Step 4 - model identification;
- Step 5 - parameter estimation;
- Step 6 - assessing model fit;
- Step 7 - model re-specification; and
- Step 8 - model cross-validation.
Basic SEM models.
This part of the course looks at the three basic types of structural equation models, namely:
- causal models for directly observed variables (regression and path analysis);
- one-factor confirmatory factor analysis (one-factor CFA — also known as one-factor congeneric measurement models), multi-factor CFA, and second-order CFA; and
- full structural equation models with latent variables (including models with mediating variables).
Problems in SEM.
This part of the course deals with problem data and difficult models including topics such as the treatment of missing data, treatment of non-continuous variables, treatment of outliers, treatment of non-normal data and small samples, constraining parameters, non-positive definite matrices, negative error variances, unidentified and inadmissible models, and recognising equivalent models.
Course Format
This course should be run in a PC computer lab or in a classroom where participants have their own notebook computers. It is also assumed that each participant has access to a computer loaded with the appropriate course software (see “Software & other requirements” section).
Recommended Background
Participants should have completed an introductory course in statistics (or have equivalent experience). Familiarity with multiple regression and factor analysis is highly desirable, as is experience with a statistical data analysis package such as IBM SPSS, SAS, or Stata. However, it is assumed that participants have had little or no experience with AMOS.
Recommended Texts
The instructor's book-length course notes will serve as the course text. Other references include:
- Arbuckle, J. L. (2021). IBM SPSS AMOS 28 User’s Guide. Amos Development Corporation.
- Byrne, Barbara M. (2016). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. (3rd Ed.) New York: Routledge Academic.
- Kline, Rex B. (2016). Principles and Practice of Structural Equation Modeling (4th Ed.). New York: Guilford Press.
- Schumacker, Randall & Lomax, Richard. (2016). A Beginner's Guide to Structural Equation Modeling. (4th Ed.). New York: Routledge
INTRODUCTION TO STRUCTURAL EQUATION MODELING (SEM) using MPLUS
About the course:
The target audience for this course is post graduate students, academic staff, and other researchers needing to learn how to run basic structural equation models using the Mplus software. This course is designed as an introduction to Structural Equation Modeling and to the use of Mplus to estimate such models.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
Course Syllabus:
Introduction, revision, and the fundamentals of SEM.
Topics include a revision of factor analysis and regression analysis and their relevance to SEM. Participants will be introduced to the Mplus program, including how to write Mplus syntax, how to run models and how to review output. We will also cover the fundamentals of SEM. Topics include: the advantages of SEM over conventional analytical techniques, the fundamentals underlying SEM, and an overview of the eight basic steps to SEM.
The eight steps of SEM.
The eight steps of SEM are covered in detail, namely:
- Step 1 - model conceptualisation;
- Step 2 - path diagram construction;
- Step 3 - model specification using Mplus syntax;
- Step 4 - model identification;
- Step 5 - parameter estimation;
- Step 6 - assessing model fit;
- Step 7 - model re-specification; and
- Step 8 - model cross-validation
Basic SEM models.
This part of the course looks at the three basic types of structural equation models, namely:
- causal models for directly observed variables (regression and path analysis);
- one-factor confirmatory factor analysis (one-factor CFA — also known as one-factor congeneric measurement models), multi-factor CFA, and second-order CFA; and
- full structural equation models with latent variables (including models with mediating variables).
Problems in SEM.
This part of the course deals with problem data and difficult models, including topics such as the treatment of missing data, treatment of non-continuous variables, treatment of outliers, treatment of non-normal data and small samples, constraining parameters, non-positive definite matrices, negative error variances, unidentified and inadmissible models, and recognising equivalent models.
Course Format
This course should be run in a PC computer lab or in a classroom where participants have their own notebook computers. It is also assumed that each participant has access to a computer loaded with the appropriate course software (see “Software & other requirements” section).
Recommended Background
Participants should have completed an introductory course in statistics (or have equivalent experience). Familiarity with multiple regression and factor analysis is highly desirable, as is experience with a statistical data analysis package such as IBM SPSS, SAS, or Stata. However, it is assumed that participants have had little or no experience with Mplus.
Recommended Texts
The instructor's course notes will serve as the course text. Other references include:
- Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén.
- Byrne, Barbara M. (2012). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. New York: Routledge Academic.
- Kline, Rex B. (2016). Principles and Practice of Structural Equation Modeling. (4th Ed.). New York: Guilford Press.
- Schumacker, Randall & Lomax, Richard. (2016). A Beginner's Guide to Structural Equation Modeling. (4th Ed.). New York: Routledge
MULTI-LEVEL MODELING (MLM) using MPLUS
About the course:
The target audience for this course is researchers needing to learn how to analyse data that has been collected through a hierarchical (or clustered) sampling approach. That is, data that is derived from subjects who are nested within groups or data that involves repeated measures that are nested within subjects — data that is multi-level in nature.
This course is designed to take participants from an introductory level up to an intermediate level of multi-level modeling. That is, the course assumes that participants have had no prior experience with multi-level modeling (or have only a basic understanding) and takes participants through the basics up to an intermediate level. Although there are several programs that can be used to conduct multi-level modeling, in this course we will use the Mplus program.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
Course Syllabus
Introduction to multilevel data, revision of basic analytical techniques, and an introduction to multi-level modeling.
This part of the course describes the nature of multilevel data, introduces the Mplus programming language (by revising basic single-level regression models and basic single-level confirmatory factor analysis), and introduces the principles and nomenclature of multi-level modeling. We will note the difference between the conventional (single-level) regression approach and the multilevel regression approach; we will highlight the dangers of not treating nested data as multilevel data; and we will discuss the advantages of multi-level modeling.
Two-level regression models.
These models investigate two-level research questions, where subjects are nested within groups and explanatory (independent) variables have been measured at the subject level (Level 1) and/or the group level (Level 2). For example, in education, our outcome variable may be “reading comprehension” and we could regress this outcome on both Level 1 independent variables (e.g. the student’s verbal reasoning skills, their motivation to learn, etc.) and Level 2 independent variables (e.g. the teachers experience, the average ability of all students within the class, etc.). In each example we will build models from the simplest variance component model to investigating random intercepts and finally, random slopes.
Two-level latent growth-curve models (repeated measure designs):
These models investigate change over time and enable the researcher to describe how an outcome (dependent) variable is improving or declining across a number of repeated measures. For example, in marketing we may be interested in analysing consumers’ attitudes to a brand over the life of a marketing campaign. The repeated measure (attitudes) may improve as a function of time, but different marketing techniques (a time-varying independent variable) may also influence the rate of improvement. Both linear and non-linear growth will be investigated.
Two-level confirmatory factor analysis (CFA) and structural equation modeling (SEM):
These models introduce latent variables into the multi-level modeling framework. In multilevel CFA models, the dependent variable is a factor (rather than an observed variable). Such models may or may not contain observed explanatory variables. In multilevel SEM the independent variable(s) is/are a factor (rather than an observed variable). Such models may contain observed and/or latent dependent variables.
Three-level models.
These models investigate three-level research questions where subjects are either nested within sub-groups and sub-groups are nested within higher level groups or repeated measures are nested within subjects who, in turn, are nested within groups. Now, not only are the explanatory (independent) variables measured at the subject level (Level 1) and/or the sub-group level (Level 2), but they may also be measured at a third level (e.g. multiple students nested within multiple classes, multiple classes nested in multiple schools).
Mixture Modeling (including Latent Class Analysis).
Mixture models are similar to multilevel models in that patterns in the data vary across different groups. The difference, however, is that group membership is determined by the data, not some a priori allocation to groups. Cross-sectional models include mixture regression analysis, Latent Class Analysis (LCA), CFA mixture modeling, and structural equation mixture modeling. Longitudinal models include Growth Mixture Modeling (GMM), for a continuous outcome, and Latent Class Growth Analysis (LCGA), for a binary, ordinal, or count outcome.
Course Format
This course should be run in a PC computer lab or in a classroom where participants have their own notebook computers. It is also assumed that each participant has access to a computer loaded with the appropriate course software (see “Software & other requirements” section).
Recommended Background
No prior knowledge of multi-level modeling is required, nor is it assumed that participants have had experience with Mplus — the Mplus programming language will be taught as part of the course. However, it is assumed that all participants will have a thorough understanding of regression analysis and factor analysis. Furthermore, it is assumed that all participants have completed a course in Structural Equation Modeling (SEM) or have had equivalent SEM experience.
Recommended Texts
The instructor's course notes will serve as the course text. Other references include:
- Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén.
- Finch, W. Holmes, and Bolin, Jocelyn E. (2017). Multi-level Modeling using Mplus. CRC Press.
- Snijders, Tom A.B. and Bosker, Roel J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. (2nd Ed.). London: Sage Publications
- Heck, Ronald H. and Thomas, Scott L. (2020). An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches. (4th Ed.). New York: Routledge.
ADVANCED STRUCTURAL EQUATION MODELING (SEM) using AMOS
About the course:
The target audience for this course is existing AMOS users wishing to expand their modeling beyond the basic models covered in the introductory course.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
Course Syllabus:
Revision of basic concepts.
This part of the course is a revision of a number of issues related to fitting structural equation models. Topics include a quick revision of factor analysis and regression and a revision of model conceptualisation, path diagrams, model specification, model identification, parameter estimation, assessing model fit, model re-specification, and model cross validation. We will also quickly revise the treatment of missing data, Bayesian approaches in AMOS to the treatment of categorical variables, treatment of outliers, model fit for skewed data (Bollen-Stein adjusted p and bootstrapped standard errors), constraining parameters, non-positive definite matrices, negative error variances, and unidentified and inadmissible models.
Constructing composite variables for use in structural equation models.
This part of the course includes a revision of one-factor congeneric measurement modeling which is then extended to introduce the Holmes-Smith & Rowe approach to using composite variables in SEM. This topic also covers reliability and validity of composites created from one-factor congeneric measurement modeling.
Advanced single-level models.
Topics include the testing of model and parameter invariance across groups (multi-group analysis), analysis of interactions with both categorical and continuous moderator variables, non-linear modeling, and mean structure analysis approaches to the Analysis of Covariance.
Multi-level Modeling and mixture models.
Introduction to the use of multilevel models to analyse data from hierarchically structured populations/samples (e.g. voters nested within electorates, students nested within classes within schools, employees nested within work groups within companies, etc.) or longitudinal studies (repeated measures nested within individuals). Topics include an overview of multi-level regression and a detailed examination of the analysis of longitudinal data using latent growth curve modeling. Mixture modeling including latent class analysis and regression mixture modeling will also be introduced.
Course Format
This course should be run in a PC computer lab or in a classroom where participants have their own notebook computers. It is also assumed that each participant has access to a computer loaded with the appropriate course software (see “Software & other requirements” section).
Recommended Background
Participants should have completed an introductory course in Structural Equation Modeling using AMOS (or have had equivalent experience). Participants should be competent in specifying models in AMOS.
Recommended Texts
The instructor's course notes will serve as the course text. Other references include:
- Arbuckle, J. L. (2021). IBM SPSS AMOS 28 User’s Guide. Amos Development Corporation.
- Byrne, Barbara M. (2016). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. (3rd Ed.) New York: Routledge Academic.
- Kline, Rex B. (2016). Principles and Practice of Structural Equation Modeling. (4th Ed.). New York: Guilford Press.
- Schumacker, Randall & Lomax, Richard. (2016). A Beginner's Guide to Structural Equation Modeling. (4th Ed.). New York: Routledge
ADVANCED STRUCTURAL EQUATION MODELING (SEM) using MPLUS
About the course:
The target audience for this course is existing Mplus users wishing to expand their modeling beyond the basic models covered in the introductory course.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
Course Syllabus:
Revision of basic concepts.
This part of the course is a revision of a number of issues related to fitting structural equation models. Topics include a quick revision of factor analysis and regression and a revision of model conceptualisation, path diagrams, model specification, model identification, parameter estimation, assessing model fit, model re-specification, and model cross validation. We will also quickly revise the treatment of missing data, approaches in Mplus for the treatment of categorical variables, treatment of outliers, model fit for skewed data (Satorra and Bentler adjusted χ2 and robust standard errors), constraining parameters, non-positive definite matrices, negative error variances, and unidentified and inadmissible models.
Constructing composite variables for use in structural equation models.
This part of the course includes a revision of one-factor congeneric measurement modeling which is then extended to introduce the Holmes-Smith & Rowe approach to using composite variables in SEM. This topic also covers reliability and validity of composites created from one-factor congeneric measurement modeling.
Advanced single-level models.
Topics include the testing of model and parameter invariance across groups (multi-group analysis), analysis of interactions with both categorical and continuous moderator variables, non-linear modeling, and mean structure analysis approaches to the Analysis of Covariance.
Multi-level Modeling and mixture models.
Introduction to the use of multilevel models to analyse data from hierarchically structured populations/samples (e.g. voters nested within electorates, students nested within classes within schools, employees nested within work groups within companies, etc.), or longitudinal studies (repeated measures nested within individuals). Topics include an overview of multi-level regression and a detailed examination of the analysis of longitudinal data using latent growth curve modeling. Mixture modeling including latent class analysis and regression mixture modeling will also be introduced.
Course Format
This course should be run in a PC computer lab or in a classroom where participants have their own notebook computers. It is also assumed that each participant has access to a computer loaded with the appropriate course software (see “Software & other requirements” section).
Recommended Background
Participants should have completed an introductory course in Structural Equation Modeling using Mplus (or have had equivalent experience). Participants should be competent in writing Mplus syntax.
Recommended Texts
The instructor's course notes will serve as the course text. Other references include:
- Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén.
- Byrne, Barbara M. (2012). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. New York: Routledge Academic.
- Kline, Rex B. (2016). Principles and Practice of Structural Equation Modeling. (4th Ed.). New York: Guilford Press.
Schumacker, Randall & Lomax, Richard. (2016). A Beginner's Guide to Structural Equation Modeling. (4th Ed.). New York: Routledge
ACSPRI COURSES
Philip Holmes-Smith developed the Applied (Introductory) and Advanced SEM courses and the Multi-level Modeling course offered by the Australian Consortium for Social and Political Research Incorporated (ACSPRI) and continues to present these ACSPRI courses himself. To view the full list of courses available through ACSPRI, go to www.acspri.org.au. These courses are usually five-day training programs offered in Canberra, Melbourne, or online in late January/early February, and in Brisbane or online in late June/early July.
Approximately 60 universities, government departments, and research organisations are members of the ACSPRI consortium. Check the website to see if your institution is a member, and contact your institution’s representative for more information. Alternatively, enrol in a course directly on the ACSPRI website.
Teaching Space
Each participant needs to have access to a desktop or notebook computer (loaded with the required software listed below) for the entire course. Typically, the teaching space is a computer lab or a classroom with tables so participants can use their notebooks. Philip will require a whiteboard in the room and a data projector with either a VGA or HDMI connection into which he can connect his own notebook.
Software Required
| SEM courses | MLM course | Other software |
| AMOS 28 (Ver. 25 min) and/or
Mplus 8.x (Ver. 8.8 preferred) |
Mplus 8.x (Ver. 8.8 preferred) | MS Office including:
Excel, Word & PowerPoint IBM SPSS 28 (or earlier) SPSS is essential for AMOS courses but is optional for Mplus courses |
Computer set-up requirements
- AMOS and Mplus both write various files to disk as they run (e.g. temporary working files, output files, path diagrams, etc.). Therefore, my example files (and any models created by the participants) must be stored on a drive that can be written to.
- AMOS must see a print driver to run. The computer does not need to be connected to a printer, but a print driver must be installed.
- I will provide access to all course files via a Dropbox link. These files should be copied onto each participant’s computer prior to the commencement of the course.
All costs for SEM or MLM courses listed below are GST inclusive and comprise a tuition fee, airfares (if applicable), accommodation (if applicable), and ground transport costs. Please note, it is assumed that courses are run in a PC computer lab or in a classroom where participants have their own notebook computers. It is also assumed that each participant has access to a computer loaded with the appropriate course software. On this basis, the total number of participants will be limited only by the number of computers available. However, feedback from past participants suggests that the total number of participants should not exceed approximately 20-25 people. The course fees listed below are a fixed cost irrespective of the number of participants.
| Location | 3-Day course only | 3-Day course
plus one additional day for one-on-one research assistance |
3-Day course
plus two additional days for one-on-one research assistance |
| Adelaide | $9,449 | $12,149 | $14,849 |
| Armidale | $11,149 | $13,699 | $16,249 |
| Ballarat | $7,950 | $10,500 | $13,050 |
| Brisbane | $9,749 | $12,449 | $15,149 |
| Canberra | $9,699 | $12,399 | $15,099 |
| Darwin | $11,119 | $13,819 | $16,519 |
| Geelong | $7,890 | $10,440 | $12,990 |
| Gold Coast | $9,179 | $11,879 | $14,579 |
| Hobart | $9,539 | $12,239 | $14,939 |
| Lismore | $10,459 | $13,009 | $15,559 |
| Melbourne | $6,752 | $8,952 | $11,152 |
| Newcastle | $10,104 | $12,654 | $15,204 |
| Perth | $11,409 | $14,109 | $16,809 |
| Rockhampton | $9,409 | $11,959 | $14,509 |
| Sunshine Coast | $10,139 | $12,689 | $15,239 |
| Sydney | $9,684 | $12,384 | $15,084 |
| Toowoomba | $10,419 | $12,969 | $15,519 |
| Townsville | $9,169 | $11,719 | $14,269 |
| Wagga Wagga | $10,559 | $13,109 | $15,659 |
| Wollongong | $9,234 | $11,784 | $14,334 |
| All Other Areas: Contact SREAMS | |||
INTERESTED IN THESE COURSES?
If you are interested in booking Phil, first check his availability. When you have found a suitable date please contact us with your desired date and time and we will get back to you as soon as possible.