Statistical Data Analysis Services

ANOVA is a statistical tool used for comparing statistical groups using the dependant and the independent variables. Analysis of variance (ANOVA) is a technique that uses a sample of observations to compare the number of means. ANOVA calculates statistical differences between two or more means for either groups or variances. The measured variables are called dependent variable e.g. Test score, while the variables which are controlled are termed as independent variable e.g. Test paper correction method. Statswork is one among the country’s leader in providing ANOVA and statistical consultancy services.  Contact Statswork for availing our services. Analysis of variance Analysis of variance (ANOVA) is a statistical technique which is used to compare datasets. It is commonly referred to as Fisher’s ANOVA or Fisher’s analysis of variance . It is similar to that of t-test and z-test, which are used to compare mean along with relative variance. However, in ANOVA, it is best suit

Factor Analysis The analysis of variance is not a mathematical theorem, but rather a convenient method of arranging the arithmetic.- Ronald Fisher The inexpensive Factor Analysis is a prominent statistical tool to identify a lot of underlying dormant factors. For more than a century it is used in psychology and also in a wide variety of situations. Factor analysis explains correlations among multiple outcomes as a result of one or more factors.   As it attempts to represent a set of variables by a smaller number, it involves data reduction.   It explores unexplained factors that represent underlying concepts that cannot be adequately measured by a single variable.   It is most popular nowadays in survey research where the responses to each question represent an outcome.   It is because multiple questions are often related and the underlying factor may influence the subject responses. The reduction technique of factor analysis in reducing a large number of variables into a f

SEM using AMOS Structural Equation Modelling (SEM) is a widely used technique in statistics to primarily study relationships based on structures. It encompasses various models involving mathematics, statistical procedures etc. This technique is known to be extremely effective when it comes to measuring latent constructs. Many of us might be familiar with concepts like Multiple Regression Analysis and Factor Analysis , this in simple term, is a combination of these techniques. It is, in fact, a mere extension of the General Linear Model. You can test a bunch of regression techniques at the same time. Structural Equation Modelling includes a model that makes room for a lot of other statistical techniques such as path analysis, confirmatory factor analysis and latent growth modelling etc. This is impressive as SEM as a type of model covers many models that are both traditional and complex. It is also effective in the assessment of variance and Multiple Regression along with e

Data Analysis should change what you do, not just how you do it. - MatinMovassate If you are to choose the right data analysis plan for your study, it is first pertinent to collect qualitative data.   Since Qualitative analysis is more about the meaning of the analysis, it is too confusing with unstructured and huge data. For conducting Data Analysis for any research, it is also important to have the right methodology. If the data and methods of data analysis plan are right, it will have numerous benefits, including making the right decisions. But before that, there are certain fundamental details to know before choosing the right data analysis plan, which includes: What is qualitative data analysis? QDA is based on interpretative policy to examine the symbolic and meaningful content of data.   In other words, it is interpreting the qualitative data by many processes and procedures to transform them into great insights for taking dynamic decisions. What is qu