10 data analysis techniques that social researchers must know

1. Exploratory factor analysis

2. Confirmatory factor analysis

3. Adapting a scale

4. Thematic analysis

5. Logistic regression

6. Case studies

7. Longitudinal analysis

8. Multi-level analysis

9. Ordinal regression

10. Social network analysis

11. Discourse analysis

12. Grounded theory

SEM Measurement Model with AMOS 18 (example 2)

1) Unconstrained Model

2) Constrained factor loading

3) Constrained structural covariance

4) Constrained measurement residuals

5) Model comparison

Assuming model Unconstrained to be correct:

Model	DF	CMIN	P	NFI Delta-1	IFI Delta-2	RFI rho-1	TLI rho2
Measurement weights	4	2.632	.621	.011	.012	-.010	-.012
Structural covariances	7	3.306	.855	.014	.015	-.023	-.026
Measurement residuals	13	11.212	.593	.048	.051	-.011	-.012

Assuming model Measurement weights to be correct:

Model	DF	CMIN	P	NFI Delta-1	IFI Delta-2	RFI rho-1	TLI rho2
Structural covariances	3	.674	.879	.003	.003	-.012	-.014
Measurement residuals	9	8.581	.477	.037	.040	-.001	-.001

Assuming model Structural covariances to be correct:

Model	DF	CMIN	P	NFI Delta-1	IFI Delta-2	RFI rho-1	TLI rho2
Measurement residuals	6	7.906	.245	.034	.037	.012	.013

A measurement model, cross-group validation, SEM, AMOS 18

First, what is 'constraint': A model of this type relates to in-variance. We want to test if factor loadings, variances of factors, covariances between factors, and variances of the 'error' terms remain the same ACROSS groups - or, if they change, the change is INSIGNIFICANT. We do that by artificially 'keep' constant one of the elements listed above (in the least restricted model, keep constant factor loadings only) or all of them (in the fully restricted model). The computer will do it for you. In AMOS, you should choose 'unstandardized estimates' if you want to see which element is kept constant.

Then we compare estimates of the less restricted model with estimates of the more restricted model, and hope that the change would be insignificant. Again, the computer will do it.

A good cross-group measurement model should have four components like above: Top left: No constraints (two models are run at the same time, chi-square difference is computed at the end). Top right: Constrained factor loadings (the least restricted model - this is minimum requirement). Bottom Left: Constrained structural covariances (the more restricted model). Bottom Right: Constrained measurement residuals (the fully restricted model).

Perfect: All models have good fit
Very good: Except the last model, other three have good fit
Good: The model constraining for factor loadings has good fit.
Poor: The model constraining for factor loadings has poor fit. In this case, we conclude that the groups that are compared do not share the same factors. Two separate measurements (one for men, another for women, for instance) should be used.

The above model has perfect fit. How can I say that?

• All 4 models have RMSEA smaller than 0.06, GFI close to 1, p value is smaller 5%
• Compared with the less constrained model, the more constrained model has a good fit and the 'gap' between 'less constrained' and 'more constrained' is insignificant. In AMOS, look for this at 'Model Comparison' tab.

Assuming model Unconstrained to be correct:

Model	DF	CMIN	P	NFI Delta-1	IFI Delta-2	RFI rho-1	TLI rho2
Measurement weights	4	2.632	.621	.011	.012	-.010	-.012
Structural covariances	7	3.306	.855	.014	.015	-.023	-.026
Measurement residuals	13	11.212	.593	.048	.051	-.011	-.012

• The model constraining for factor loadings is  insignificantly different from the unconstrained model.

Figure Caption in SEM

This is the text to use:

chi squared = \cmin
df = \df
p = \p
RMR = \rmr
GFI = \gfi
RMSEA = \rmsea


This is the complete list:
\cmin is a "text macro", a code that Amos fills in with the minimum value of the discrepancy function, C (see Appendix B), once the minimum value is known. Similarly, \df is a text macro that Amos fills in with the number of degrees of freedom for testing the model and \p is a text macro that Amos fills in with the "p value" for testing the null hypothesis that the model is correct. Here is a list of text macros.
\agfi Adjusted goodness of fit index (AGFI)
\aic Akaike information criterion (AIC)
\bcc Browne-Cudeck criterion (BCC)
\bic Bayes information criterion (BIC)
\caic Consistent AIC (CAIC)
\cfi Comparative fit index (CFI)
\cmin Minimum value of the discrepancy function C in Appendix B
\cmindf Minimum value of the discrepancy function divided by degrees of freedom
\datafilename The name of the data file. \longdatafilename displays the fully qualified path name of the data file.
\datatablename The name of the data table (for those file formats that allow a single file to contain multiple data tables, such as Excel workbooks.)
\date Today's date in short format. \longdate displays today's date in long format. The displayed date is made current whenever the path diagram is read from a file, saved or printed.
\df Degrees of freedom
\ecvi Expected cross-validation index (ECVI)
\ecvihi Upper bound of 90% confidence interval on ECVI
\ecvilo Lower bound of 90% confidence interval on ECVI
\f0 Estimated population discrepancy (F0)
\f0hi Upper bound of 90% confidence interval on F0
\f0lo Lower bound of 90% confidence interval on F0
\filename Name of the current AMW file. Use \longfilename to display the complete path to the current AMW file.
\fmin Minimum value of discrepancy function F in Appendix B
\format Format name (See Formats tab.)
\gfi Goodness of fit index (GFI)
\group Group name (See Manage groups.)
\hfive Hoelter's critical N for =.05
\hone Hoelter's critical N for =.01
\ifi Incremental fit index (IFI)
\longdatafilenameThe fully qualified path name of the data file. \datafilename displays the data file name without the path.
\longdate Today's date in long format. \date display's today's date in short format. The displayed date is made current whenever the path diagram is read from a file, saved or printed.
\longfilename Fully qualified path name of the current AMW file. Use \filename to display the file name without the path.
\longtime The time in long format. \time displays the time in short format. The displayed time is made current whenever the path diagram is read from a file, saved or printed.
\mecvi Modified ECVI (MECVI)
\model Model name (See Manage models.)
\ncp Estimate of non-centrality parameter (NCP)
\ncphi Upper bound of 90% confidence interval on NCP
\ncplo Lower bound of 90% confidence interval on NCP
\nfi Normed fit index (NFI)
\npar Number of distinct parameters
\p "p value" associated with discrepancy function (test of perfect fit)
\pcfi Parsimonious comparative fit index (PCFI)
\pclose "p value" for testing the null hypothesis of close fit (RMSEA < .05)
\pgfi Parsimonious goodness of fit index (PGFI)
\pnfi Parsimonious normed fit index (PNFI)
\pratio Parsimony ratio
\rfi Relative fit index
\rmr Root mean square residual
\rmsea Root mean square error of approximation (RMSEA)
\rmseahi Upper bound of 90% confidence interval on RMSEA
\rmsealo Lower bound of 90% confidence interval on RMSEA
\time The time in short format. \longtime displays the time in long format. The displayed time is made current whenever the path diagram is read from a file, saved or printed.
\tli Tucker-Lewis index (TLI)

Two-way relation between interethnic social capital and drunk driving, risky drinking, binge drinking

 

 

Interethnic social capital is measured by frequency of interethnic drinking (at least monthly)

 

This is the model for drunk driving:

 

This is the model for binge drinking (expert definition, drinking 6 cups or more at a time)

 

This is the model for risky drinking (expert definition, AUDIT >7 for men)

This is the model for binge drinking (an ‘agreed’ version between ‘expert knowledge’ and ‘lay knowledge’)

This is the model for risky drinking (an ‘agreed’ version between ‘expert knowledge’ and ‘lay knowledge’)

Neutralization, for men