Data Analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Mainly methods for Data Analysis in Iraniu are include:
Structural equation model (SEM)
Structural equation models are often used to achieve unobservable 'latent' constructs. They often invoke a measurement model that defines latent variables using one or more observed variables, and a structural model that imputes relationships between latent variables.
In SEM, interest usually focuses on latent constructs--abstract psychological variables like "intelligence" or "attitude toward the brand"--rather than on the manifest variables used to measure these constructs. Measurement is recognized as difficult and error-prone. By explicitly modeling measurement error, SEM users seek to derive unbiased estimates for the relations between latent constructs. To this end, SEM allows multiple measures to be associated with a single latent construct.
Our regression modelling looking for understand key items that influence trends and responses. We use our team strong knowledge at statistics with professional’s packages to estimate and analyze market movements. These reports will help business owners to statistically understand what are the items are affect market trends and also the intensity of this influence
Path analysis is special case of SEM, in this method we break multiple regression between different variables to find out indirect relationships. Path analysis is an extension of the regression model. In a path analysis model from the correlation matrix, two or more casual models are compared. The path of the model is shown by a square and an arrow, which shows the causation. Regression weight is predicated by the model. Then the goodness of fit statistic is calculated in order to see the fitting of the mode
We are looking at data in different way. We use different methods to understand what models and patterns can be dig out basis market structure from mass data. Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills.
Content Analysis refers to methods for studying and/or retrieving meaningful information from documents. In a more focused way, content analysis refers to a family of techniques for studying the mute evidence of texts and artifacts.
Analysis Variance (ANOVA)
This statistical method is commonly used for comparing means, variance or defined quantitative numbers among different groups of consumers or people. We use this method in our research when we are facing different types of consumers with differences in age, social class, or even differences in the way of thinking and images to find out, is the difference significant or not and then what are each groups specifications.