How do I Choose a Statistical Software

The world around is talking about data analysis. Whether one talks about analysis of consumer behaviour or a perspective to the critical metrics of six sigma or maybe any other programme that is actually data driven it is all related to data analysis. The good news here is that not only is the available data more than ever before but there are available an enormous range of software options which simplify the understanding of the data which otherwise is difficult to comprehend.

The options that are available for data analysis have a large gamut of options available. They range and vary from paper pencil option to a calculator to a more customized system that would very precisely take care of the smallest of detail tailored as per the needs of the researcher. However they would cost much more than the conventional systems and could go up to millions of rupees.

Talking of the extreme ends of the gamut, unless the researcher really enjoys calculations on fingers or has a huge amount of money lying idol to splurge, a software package sitting somewhere between these two extremes  is what would work the best. However, that still leaves the researcher a wide variety of software packages to choose from and one needs to administer in a research.

When one talks about picking up data analysis software, there isn’t any right or wrong choice. What works best for a particular researcher may depend on more than one factor.

The first factor to consider is to analyse the person who will be using the software. The statistical skills of the person in terms of, whether he is an expert, novice or a blend of both. Will the data be analysed day in day out or once in a while? When this is figured out, it helps to match options with needs so as it can be avoided to choose any wrong package  that is either too difficult to handle or does the wrong thing entirely

It is vital you pick approach research methodologies and methods for your thesis – your research after all is what your whole dissertation will rest on. When the researcher is willing to collect quantitative data it means that the variables are being measured and existing hypotheses are being verified and questioned. Often data is used to generate new hypotheses which are based upon data that is collected on different variables. If one would try to compare qualitative data with Quantitative data, it could be compared on the following parameters.

Goal or Aim: The aim of qualitative research is more exploratory in nature. It provides a more detailed description of the research topic. On the other hand, quantitative research is more focussed on counting and classifying the features that comprise statistical models and figures which target to explain the observation.

Usage: In the beginning phase of the research qualitative research serves a better purpose and when one talks about the latter part of the research, quantitative research has more weightage. A clearer picture about what to expect from research is drawn from quantitative research vis a vis qualitative research.

Data Gathering: In the case of qualitative research, the main data gathering instrument is the researcher. The different strategies that the researcher employs depend largely upon the approach of the research. Some of the examples of the techniques in qualitative research are in depth interviews, structured unstructured interviews, narratives etc. When one talks of quantitative research tools, the instruments used are questionnaires and surveys to collect numerical data which is measurable.

Presentation of the Data:  In the case where the data is of qualitative nature, in any of the forms such as words, images or objects, it appears in the form of graphical figures. On the other hand, if the research is quantitative in nature, the tabular representation of data is there which is in the form of numbers or statistics

A hypothesis is a tool of quantitative studies. It is a tentative prediction regarding the relationship between the variables which are being studied. The key work that the hypotheses do is that it translates the research question into a prediction of the outcomes that can be expected from it. The entire research is done as an attempt to approve or disapprove the hypotheses.

In order to be complete, it is important that a hypotheses includes these three main components:

• The variables
• The Population
• The relationship

The key features of hypotheses are:

• Stated clearly  by using  the appropriate terminology
• Testable
• It should be a clear about the relationship between the variables
•  It should be having  definable, limited scope

There is more than one type of hypotheses. They are:

• Simple Hypotheses: These hypotheses help to predict the relationship between a single independent variable (IV)  on one side and a dependant variable(DV).
• Complex Hypotheses:  This kind of hypotheses helps to predict the relationship that is there between more than two or two independent variable and likewise two or more than two dependant variable.
• Directional Hypotheses: These kinds of hypotheses are drawn from theory. These imply that the researcher is committed to a particular kind of outcome. These kind of hypotheses
• Non-directional Hypotheses: These kinds of hypotheses are used when there is little or no theory or when the findings are contradictory to previous study.  They may have impartial implication and do not stipulate the direction of the relationship.
• Associative and causal hypotheses:  These kind of hypotheses propose relationships between two variables. In this case when one variable changes the other one also changes.
• Null Hypotheses: As the name is indicative, they are used when the researcher insists that there is no relationship between the variables or when the empirical data is inadequate to state any kind of hypotheses. Null hypotheses can be simple, complex, causal and associative.
• Testable Hypotheses: It includes those variables that can be measured or have the capacity to be manipulated. Their task is to predict a relationship on the basis of data.

Cluster Analysis is a grouping technique.  This technique works on an assumption that states that the similarity is dependent upon multiple variables. It helps to measure the proximity of the study variables. The groups that emerge out of cluster analysis are homogeneous in their own composition and heterogeneous when it comes to comparison to other groups. The grouping for cluster analysis can be done for anything ranging from objects, individuals to products and entities. The researcher identifies a set of clustering variables. These variables are the identified variables that have a significant role in classifying the objects into various groups. For this reason cluster analysis is also called a classification or grouping technique. It has a lot of use in different branches of social sciences particularly psychology, sociology, management and engineering.

Cluster analysis is different from other data reduction techniques. The similarity of course is that it analysis the function of multiple independent variables but the difference is that, in factor analysis the original correlated variables are reduced to a more manageable number gut the data reduction is carried out on the  columns of the data matrix. While, in the case of cluster analysis, the focus is on the rows which could be the individuals, entities, products or any other variable.

Another data reduction technique that can be confused with cluster analysis is the discriminant analysis. In the discriminant analysis the classification and identification of similarities is a pre requisite. It is imperative here to put across the objectives and rules of similarities in order. In the case of cluster analysis,  the hole population is undifferentiated and all efforts to find out the similarity in the response to variables and the grouping task is done as an outcome of the cluster analysis.

The usage of cluster analysis is widespread and it has application in all the varied branches. It is the best classification technique when the factors involved in data collection are multiple. Its main use is seen in the segmentation technique where the main task is to split the potential customers within a market into different groups. Maximum explanation from the output of the cluster analysis has been witnessed in this field of segmentation.