Mixed effects modelling and multi-level modelling are used where the data is stratified by additional characteristics or conditions. For example, if you have student data collected from a large number of different schools, then the data entries corresponding to different students belonging to the same class and to the same school are not fully independent, as these students may be affected by the specific experiences produced by the school and class/teacher. Thus, such data has several levels of stratification - school level, class level, etc. Analysis of this data must be conducted using multi-level modelling. 

Structural equation modelling (SEM) is one of the most popular and powerful statistical techniques for the analysis of data resulting from surveys and questionnaires, population health research, social sciences, education, psychology, business management, etc. It is highly useful in any area where numerous and mutually correlated variables are considered. The major benefit of these models is that they enable construction of networks of direct and indirect effects between the variables, thus taking into account their mutual correlations and enabling visualisation of interaction paths leading towards the dependent variable(s) of interest.  

'Factor analysis' refers to statistical techniques developed for identification and characterisation of statistical constructs (also called as 'factors' or 'latent variables'). Statistical constructs are typically constructed of several variables or items that are measured directly and reflect, or associated with, the same aspect or attribute. Constructs are not normally measured directly (unlike the variables constructing them), which is why they are often termed as 'latent variables' characterised by their factor scores. Constructs or factors describe and quantify mutual characteristic(s) of the associated measured variables and represent those measured variables as an overarching general characteristic or attribute. 

Survival analysis is a colloquial term including several statistical methods for consideration of survival events occurring at different moments in time. The survival events could be anything including death, occurrence or recurrence of a disease, childbirth, buying or selling property, marriage, divorce, etc. Survival analysis allows calculation of survival rates and hazard. Hazard is the probability that the survival event will occur, per unit of time, after the subject survives a particular time interval without the event. This probability as a function of time is called the hazard function. 

ROC regression analysis is an advanced new method for the determination or validation of diagnostic and operational criteria and characteristic thresholds. For example, ROC regression can be used to establish (or validate) criteria for healthy and sick individuals on the basis of their body temperature. If the body temperature is below 37 ^oC, then the person is likely to be healthy, whereas if the body temperature is above 37 ^oC, then the person is likely to be sick. The body temperature of 37 ^oC is considered as the threshold, while the body temperature variable can be regarded as a biomarker for the state of health (at least in the event of particular health conditions).