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Between Balanced and Unbalanced Scales

It is interesting and important at the same time to understand whether it is best to administer a balanced scale or an unbalanced scale.  To begin with, if we focus on the balance scales, it primarily means that there an even number of points on either side of the scale. This means on the positive … Continue reading “Between Balanced and Unbalanced Scales”

It is interesting and important at the same time to understand whether it is best to administer a balanced scale or an unbalanced scale.  To begin with, if we focus on the balance scales, it primarily means that there an even number of points on either side of the scale. This means on the positive as well as the negative side.

On the contrary, a negative scale is something that is skewed on either the positive or  the negative side. This is again an option to be used when the research scale  had to be selected.

The balanced  scale is bigger and gives more  narrower options to the respondents, to chose between balanced and unbalanced scales, it largely depends upon the  the kind of expectation or estimation that  you have from the responses. If the researcher is aware that most of the respondents are going to agree to him the breakdown of the  negative side of the scake will not be of much use and it is better to use an unbalanced scale.  To explain it simply, in the case of the predicted skewed response on either side of the scale from the respondents, an unbalanced scale works well. However, if the researcher has doubt about the responses and cannot be sure if the responses will skew on either side then he should go for a larger scale like a balanced scale so that fair anount of clear options are offered to the respondents.

One point that should be noted in  the case of unbalanced scale and should be considered thoroughly before deciding the scale is that, unbalanced scale do not offer a middle point and the result is an ordinal scale aas opposed to an interval scale. This makes it difficult to compute  the mean or average response. And  for the measures of central tendency,n the dependance comes on median instead of  mean.

Considering all these factors, it becomes simpler for a researcher to chose between a balanced and unbalanced scale.

Cluster Analysis: A Classification Technique

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 … Continue reading “Cluster Analysis: A Classification Technique”

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.

Participatory Research: Methods and Practices

The participatory methods are focussed towards the planning and conducting the research process in the presence and involvement of people, those people whose life, world, actions and thoughts are under the process of research. As a consequence of this, the means and methods of enquiry and the different questions of research are created from the … Continue reading “Participatory Research: Methods and Practices”

The participatory methods are focussed towards the planning and conducting the research process in the presence and involvement of people, those people whose life, world, actions and thoughts are under the process of research. As a consequence of this, the means and methods of enquiry and the different questions of research are created from the synchronisation of two different views and practices, involving both practice as well as science. Research bestows its benefits on both the sides. Practices that have been established as daily basis activities bring forth their own view point and perspective, always a new way to deal with a challenge. The participatory research process widens the horizons of the researcher and offers the freedom to co researchers to move beyond the limitations of standard routines, interactions in a more cognitive fashion. It helps to challenge and rework on established practices and interpretations. However, just by conducting participatory research, it is not ensured that there would be a convergence of science and practice. It is much more than that and a demanding process that is developed on two different spheres of action, both science and practice come into coherence with each other, interacts, develop lead to an understanding of each other.

The participatory method is an orientation of enquiry. Participatory research can be called a method that challenges concrete and fundamental research methods and supports the benefits of research that may lead to the involvement of the research partners to enhance productivity of knowledge. The approaches suggested for participatory research are not different and distinct from the standard empirical social procedures. The main characteristic of participatory research is it’s individuality and self-determination. It is not suggested and possible to canonize them in the form of a single, cohesive, methodological approach. Incorporation of processes is all the more important in participative research than in any other. If the purpose and desire is to gain a further deep understanding of the contextual structured of meaning and the dynamism that in embedded in social action, it becomes worthy to incorporate participatory research at the design stage itself.

To conclude, participatory research does pose certain questions in context to knowledge and research in a very radical fashion. It is so strong an area of research that it has the capacity to bring attention to the neglected areas of the methodology and kindle their further development.

Correlation

Correlation is used to measure the degree of association that is there between two variables. Whenever the researcher is dealing with two variables, the talk is about simple correlation and when the involvement is of more than two variables. On the other hand, regression is used to explain the variations that happen in one variable. … Continue reading “Correlation”

Correlation is used to measure the degree of association that is there between two variables. Whenever the researcher is dealing with two variables, the talk is about simple correlation and when the involvement is of more than two variables. On the other hand, regression is used to explain the variations that happen in one variable. This is usually referred to as the changes made in the dependant variable by the independent variable. It helps to identify the nature of the relationship. It is not necessary that the independent variable in regression would be only one. They can be more than one also. When there is only one independent variable, we call it simple regression and when   there is more than one variable, we call it multiple regression analysis.

Correlation helps in measuring the degree of association that is there between two or more variables. When the research is dealing with two variables, then the correlation applicable is simple correlation. When the involvement is of more than two variables then the subject matter moves towards multiple correlation. Correlation is largely of three types:

  • Positive Correlation:  When the two variables X and Y move in the same direction, it is said that the correlation between the two is positive. If one variable increases the other variable also increases and likewise in the situation of one variable decreasing, the other one also decreases.
  • Negative Correlation:  In the situation when the two variables X and Y move in the opposite direction, it is called as negative correlation. In the case when one variable increases and the other variable decreases or vice versa.
  • Zero Correlation:  When the correlation between two variables X and Y is completely null or zero. The increase or decrease in Y is not dependent upon an increase or decrease in X.

If the correlation coefficient is equal to 1, the two variables are said to be positively correlated. If the coefficient of correlation is -1 the variables are said to be lying on a  negatively sloped straight line

Factor Analysis

Factor Analysis has proven to be a very useful technique in the field of market research and analysis. Its various uses are: • It helps to make sense of a big data which has interlinked relationships • It helps to decipher relationships that have been hidden • It helps to surface up the underlying relationship … Continue reading “Factor Analysis”

Factor Analysis has proven to be a very useful technique in the field of market research and analysis. Its various uses are:

• It helps to make sense of a big data which has interlinked relationships
• It helps to decipher relationships that have been hidden
• It helps to surface up the underlying relationship that is there between tastes preferences of consumers where factor analysis is largely used.
• It helps in the condensing of the data
• It helps in correlating the data and draw conclusions from the gathered data.
• Helps in formation of the empirical clusters.

Types of Factor Analysis:
The larger use of factor analysis is for understanding the interpretation of data and analysing the underlying relationship that exists between variables and the other underlying factors. Factor analysis works beyond grouping responses and their types; on the other hand it segregates the variables and then groups them according to their co relevance.

Factor Analysis can largely be segregated into three categories, depending upon its varied use in the market.

• Exploratory Factor Analysis
• Confirmatory Factor Analysis
• Structural Equation Modelling

The exploratory factor analysis is used for the measurement of the underlying factors that have an effect on the variables in the data structure. This is done without any biased perspective and setting a pre-defined structure to the outcome. The second kind of factor analysis, which is the confirmatory factor analysis is used to confirm the correlation in the existing set of the factor that have been predefined and the different variables that affect these factors. The third type of factor analysis which is called the structural modelling hypothesises the relationship between a set of variables. It can be used for both exploratory as well as confirmatory modelling.

Factor analysis would yield accurate and beneficial results only when the expertise of the researcher is there in selecting the data and assigning it the attributes. Choosing of the correct factors so as to avoid a lot of overlapping in characteristics is also very important. If done in the right manner, factor analysis would assist in very critical decision making. It is particularly useful in consumer behaviour studies and it woyld help in product development, pricing segmentation, penetration, distribution, pricing and other important decisions.