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Why use SPSS AMOS for Structural Equation Modelling (SEM)?

Analyzing structural relationships is made feasible with the help of Structural Equation Modelling. It is the amalgamation of factor analysis and regression analysis. These techniques are used to study the relationship between structural variables and constructs in latent variables. This method is preferred by researchers because of its ability to perform multiple operations in a single analysis. 

SEM is applied in building complex models using latent variables. A graphical or programming interface is necessary to observe and obtain variables, such as SPSS AMOS software. 

Characteristics of efficient SEM

  1. SEM facilitates controlling and understanding of the analyzes.
  2. Assumptions derived from the different analysis techniques are transparent in nature and could be tested.
  3. Creative graphical user interface is employed and it allows rapid model debugging.
  4. Model fit and individual parameter estimate tests are run alongside the process.
  5. Models involving estimation of the relationship between latent variables are kept free from errors that could arise in measurement.
  6. Models are customized to fit using different database of error structures that are autocorrelated.
  7. Multiple linear models can be fit under a single unifying framework.

About SPSS AMOS

Being an additional SPSS module, SPSS AMOS is specially used for performing structural equation modelling, path analysis, confirmatory factor analysis (CFA). AMOS is programmed to represent SPSS derived results in visual models, during structural equation modelling.

Attributes of  SPSS AMOS

  1. Simple drawing tools are used for drawing models.
  2. Computations regarding SEM are quickly resolved.
  3. Results obtained by both graphical and non-graphical applications.
  4. Various methods are used to calculate SEM coefficients. 
  5. Manual drawing of model is allowed.

How SEM is executed by using SPSS AMOS?

  • Attach data – By choosing suitable file name, data to be used for SEM analysis is attached in the SPSS AMOS software. 
  • Represent cause-effect relationship – Using a single headed arrow to draw the cause-effect relationship between the unobserved and observed variables. 
  • Name the variable – Using object properties in a graphical window, the variables in the AMOS is named. 

A model that is consistent in its theoretical understandings and parameter estimations using respective statistics is significant for research purposes. It falls on the researchers to rule out occurring model alternatives by efficiently testing and identifying fitting models. Hence, SEM using SPSS AMOS alleviates that and contributes to better research knowledge.

Purpose of using SPSS AMOS to obtain SEM results

SEM is conducted using AMOS specifically for the following advantages:

  1. The number of variables used and the variables used for the purpose of SEM analysis can be studied using the text output variable summary.
  2. The observed variables and unobserved variables are differentiated from the model.
  3. To ensure the normal distribution of the SEM model, different tests are used.
  4. These tests derived outputs with normality of data.
  5. Proper estimates of different effects that have on data is provided in output.
  6. The different tests performed on regression weight, direct, indirect, total effect affecting standardized loading of factor, residua, correlation and covariance to study effects and produce appropriate results.
  7. The statistics of the model are ensured to fit the desired results.
  8. Complications arising while drawing the model are prompted by error messages or failure to deliver the output.

Merits of SEM 

SEM involves using confirmatory path analysis which comes to the rescue by avoiding possible mistakes. Here, factors are specified exactly in numbers and loading patterns.

Since SEM uses different analysis techniques, it facilitates examination of more than one at a time. It replaces EFA and Regression by considering potential measurement errors too.

Quick tips to condense word count in your dissertation

A dissertation is usually around 80,000 words. As a research candidate, you strive hard to stick to the word limit in your dissertation, lest you get penalised.

Therefore, before beginning with your writing process, always create a rough framework of your dissertation and allocate a certain word limit to each of its respective sections. When you begin writing with a pre-decided word limit in your mind, you would focus more on the central idea of the research i.e. relevant aspects. You should ignore irrelevant facts and try making shorter sentences by making use of bullets.

Going a little above or below the prescribed word limit (10% of the whole text) is generally acceptable. But, what if you cannot manage to do so? Here are a few quick tips to reduce your word count after you have finished writing your dissertation –

Describing Research Findings
Instead of describing your research findings in this way –

It was found that the mice in the research group A were less receptive to the medication as compared to group B. (22 words)

You can write a concise statement in the following way –
The mice in the research group A were less receptive to the medication as compared to group B. (18 words)

Delete adverbs
Spot all adverbs i.e. all words in your text ending with a – ly and replace them with a single more word to decrease the word limit. For example –  


Richard ran quickly towards the door to open it. (9 words)

Richard hurried towards the door to open it. (8 words)


Remove Verbosity & Redundancies –  Prune the sentences and remove all words which induce wordiness in the text. For example –

The actor with versatility, ……………………

The versatile actor, ………………………….

Here replacing the noun versatility with the adjective versatile cuts out the wordiness.

Also, remove redundant elements from the text like in the following example –

Mark was extremely irate, displeased and annoyed. (7 words)

Mark was annoyed. (3 words)

The above three adjectives are synonymous and therefore, using them all in the same sentence hardly provide any added value to the sentence.

Contract Compound Verbs – Contract compound words in the following way to reduce the word count by half –

Have not – haven’t
Does not – doesn’t
Will not – won’t
Could not – couldn’t
Should not – shouldn’t

Remove Prepositions – Removing prepositions can provide lucidity to your content. For example –

Tea from Turkey – Turkish Tea

Writer of detective fiction – Detective Fiction Writer

Make Judicious Use of Tables & Figures
Tables and figures are exempted from the word count. Therefore, try to place your data in a tabular form instead of lengthy paragraphs.

Prefer Active Voice Over Passive Voice
Active voice simplifies the sentences and improves readability. For example –

The letter was taken by Gillian and was delivered to the manager immediately. (13 words)

Gillian took the letter and delivered it to the manager immediately. (11 words)

Hyphenated words
The easiest way to deceive the automated word processing systems is by making use of hyphenated words. As these words are counted as one even though they occupy the same space. Few examples are –  half-serious, half-hearted, cross-complaint, bail-out, build-up etc.

Although, these are minor alterations, self – editing can reduce the word count to a certain extent, if done extensively. Yet, for substantive editing, it is advised to take help of a professional academic editor  as they can effortlessly maintain brevity in the text without compromising on the quality.

Dissertation Data Analysis – Dissertation India’s Guide to Statistics for Dissertation

After having collected the data, the next stage is data analysis. But before you actually set out to collect the data, your research strategy should be ready.  This is because you must know  the data analysis technique to be used before your proceed for data collection.

What statistical tests would be appropriate for your study would depend on the following factors:

  • Your Research Hypotheses
  • The Research Design in use
  • The type of Data

You must be very clear about your research question or what you call as research hypotheses before creating the design for your research. Your hypotheses and design, in principle, would let you know the which statistical tests would be the most appropriate to run on your data so that your research questions are answered in the most appropriate manner.

Few important things to take care of when taking up your data analysis are:

  • Relevance:  You must never blindly follow the collected data. All the presented data must be relevant and appropriate to the aims of the research and you must be able to give the applicable logic for data selection and analysis. You should justify your statistical techniques with the same strength as the data collection tools. It must convince that your method was not chosen haphazardly and rather was decided after prolonged research and reasoning.

 

  • Thoroughness of work: You should thoroughly analyse all data and use it to demonstrate   the critical perspective, keeping in mind the scope of errors. It is important to accept your limitation and strengths to add credibility to your research.

 

  • Presentational Device: When the data is large in number, it becomes a problem to represent this data. Consider all available means to present what is collected by means of Charts, Graphs, Diagrams, Quotes, formulae, tables etc. You must keep your reader in mind while presenting your data, which means that  your analysis should also be clear to someone who is not familiar with your research.
  • Discussion: In the discussion aspect of your data analysis you will have to exhibit the different trends, patterns and themes that can be figured out from the data. Assess the significance of each theoretical interpretation on the other.

 

 

Findings: This should include the essential conclusive outcomes that emerge from your data analysis. Findings should be clearly stated and the assertions should be backed by clearly stated and argued reasoning and empirical support.

A sneak peek to performing structural equation modeling using AMOS

Research often involves testing the relationship between variables in a hypothesis. While various quantitative techniques can be used for this purpose, it is structural equation modeling(SEM) approach that provides a visual & easy to interpret display of the causal relationship between the variables. 

Structural equation modeling, a combination of factor and multiple regression analysis, is a multivariate statistical analysis technique that evaluates the structural relationships between latent constructs and measured variables. Structural equation modeling is of two types.

  1. Measurement model 

This type of model represents the theory specifying how the measured variables group together to demonstrate the theory.

 

  • Structure model 

 

Here, the theory defining how the constructs are related to other constructs are represented. 

In research, SEM technique depends on the popular statistical software known as Analysis of Moment Structures (AMOS). This is because AMOS produces tabular outputs and graphic models using user-friendly tool. However, prior to performing SEM, it is a must to consider few assumptions such as:

  • Linearity – There should be a linear relationship between the endogenous and exogenous variables. 
  • Outlier – Since outlier impacts the model significance, the data should be free of outliers.
  • Multivariate normal distribution – Maximum likelihood approach is used for multivariate distribution. Also, the small changes in the multivariate results in the larger difference in chi-square test. 
  • Sequence – There exists a cause and effect relationship between the exogenous and endogenous variables.  
  • Model identification – The models must be exactly or over-identified as the under-identified models aren’t considered. 
  • Uncorrelated error terms – The error terms are uncorrelated with other variable error terms. 
  • Non-spurious relationship – This implies that the observed variance should be true. 

If all the above-mentioned assumptions hold good for SEM, AMOS then continues with the structuring equation modeling process by assuming that the data has been modeled. Some of the methods used by AMOS to perform SEM include:

  1. Generalized least squares – This method estimates the coefficients in the linear regression model if there exists correlation amongst the residuals. 
  2. Unweighted least squares – This approach estimates residual errors to access the conditional mean. 
  3. Browne’s asymptotic distribution free – This type of method is largely recommended when samples containing the non-normal data and SEM involves analysis of covariance structure. 

Model construction in AMOS

On determining the type of model, the next step is to run AMOS by clicking ‘start’ menu and choosing the ‘AMOS graphic’ option. When the AMOS starts running, a window known as ‘AMOS graphic’ appears, in which the user can manually draw the SEM model.

  • Data input – Choose the file name from the data file option and attach the data in AMOS for further SEM analysis. The user can also select this option by clicking the ‘select data’ icon. 
  • Observed variable – Use rectangle icon and draw the observed variables.
  • Unobserved variable – Deploy circle icon and draw the unobserved variables. 
  • Covariance – To denote the covariance between variables, select a double-headed arrow.
  • Cause-effect relationship – To establish the relationship between the observed and unobserved variable, use single-headed arrow in AMOS.  
  • Naming the variable – It is important to determine the variables to work with them precisely. Click on the variable in the graphical window, select ‘object properties’ option and name the variables in the AMOS. 
  • Error term – The error term appears next to the unobserved variable and is often used to draw the latent variable. 

After running the analysis, the outputs are displayed on the graphic window. However, the graphic window will display only the error term weights, standardized & unstandardized regressions. Some of the results produced by AMOS include:

  • Variable summary 

AMOS and the text output variable provides the option of viewing how many variables and which variables have been used for SEM analysis process. The user can also the number of observed and unobserved variables present in the model. 

  • Accessing the normality 

In the SEM model, data must be normally distributed. AMOS provides skewness, text outputs, Mahalanobis d-squared test, Kurtosis and also gives information about the normality of the data.

 Modification index 

The modification index describes the reliability of the path in the SEM model. If the modification index value is huge, then more paths can be added to the SEM model.

  • Estimates 

The estimate option in the AMOS text output will provide the output for regression weight, residual, standardised loading factor, covariance, indirect effect, direct effect, correlation, total effect, and many more. 

  • Error message 

If there is any error in the SEM model drawing process, then AMOS will either give an error message or will not calculate the result. 

  • Model fit 

The model fit will provide the result for goodness fit model statistics and present goodness fit indexes such as RMR, GFI, BCI, TLI, RMSER, and many more.

 Additionally, AMOS enables the functioning of SEM analysis and makes it easy to arrive at the statistics (where direct measurements are not possible). 

The Need of SPSS for a Successful Dissertation

Most candidates of US who are in the process of completing a dissertation or master’s thesis presently or in future have a common question to ask that is whether it is necessary to learn SPSS (Statistical Package for Social Sciences) for the preparation of a successful dissertation project. At some point of time it is necessary to know SPSS for the benefit of your project work. So many graduate and doctoral programs focused mainly on research related work exposes their students to the need for learning SPSS and its proper application in the dissertation process. The SPSS statistics software has an important application in the dissertation work and researchers use it to perform statistical analysis of their research work. The software can import any form of dissertation data for analysis purpose, to generate charts, tabulated data and descriptive statistics as well.

Learning the use of SPSS
There are various consulting firms and companies which helps you to learn SPSS so that you can benefit from it in your dissertations. Although apparently it might seem quite easy to use the features of this software but gradually you will realize the need for an expert guidance to operate the software properly. There are various steps which each candidate must learn to successfully avail the benefits of the SPSS statistical package. The first thing is to learn the method of entering dissertation data in SPSS, the next step being the process of entering and labeling dissertation variables in SPSS. The final stage is the way you should read the dissertation statistical analyses prepared in SPSS. All these take a considerable amount of time and every candidate must learn it in depth to smoothly glide through the process of dissertation work. Each and every step and the sub steps within them are equally important to carry out dissertations in SPSS. There are also various online consultation services which provide the facility of teaching SPSS where candidates all over the world can have the privilege of learning this easy-to-use statistical package.

Benefits of outsourcing such task
Many US doctoral candidates have a dilemma in their mind whether to learn the mechanism of SPSS or outsource such task to a consulting firm. Though some people may be of the opinion that learning to use SPSS should be beneficial as it is only the individual who knows best how to present their work but from a practical viewpoint it is both safe and convenient to outsource the task of SPSS to a consulting firm. There are several reasons for this; firstly you won’t need to spend your valuable time learning SPSS for your research work and fully concentrate on the dissertation you are preparing. Moreover you can avail the services of expert professionals to carry out the SPSS task for you and make your dissertation more presentable and acceptable. So it should be sensible to outsource such task to companies like Statistics Consultation rather than spending 500 hours in learning and mastering such a software which would be of no use after you finish your research work.