Questionnaire Design

We believe that Questionnaire designing is the most intense and integral part of any research that is conducted worldwide.

We at  Malana Research Consult (MRC), go deep in understanding clients requirement as to what exactly they are looking for and how the research can really help them pull out the information which is most important for their business.

Once, the requirements are clear; we work towards making a questionnaire which is user friendly and in a flow to fulfill the research needs. We believe in Excellent delivering rather than depending on the quantity of questions in the survey.



Analysis of data is a process of reviewing, cleaning, converting, and exhibiting data through systematic application of statistical and/or logical techniques with the objective of ascertaining valuable information, signifying inferences, and assisting decision-making. While data analysis in qualitative research can include statistical techniques, several times examination becomes a continuing iterative process where data is constantly collected and scrutinized concurrently. Actually, researchers and analysts usually evaluate for patterns in findings through complete data collection stage. The method of the analysis is generally based on the precise qualitative approach followed and the form of the data. A vital element of confirming data integrity is the correct and suitable exploration of research outcomes.

Presenting the findings after data collection is an art through clear as well as detailed report. Generally, report does have different sections based on the topic of the study and the type of study undertaken. Section wise, different components include – Title section, summary, introduction, body, conclusion, recommendations. Key here is – the language used should be kept as simple as possible, and clear. It should convey the actual message and the findings of the study after thorough analysis plus should be completely devoid of un-necessary information.

We, at Malana Research Consult do have a team of expert, well-trained and experienced research analysts, researchers and report writers. Our team is well-balanced in a way that the dedicated person in research team does have thorough understanding of the industry domain, along with enriched experience in market research industry accompanied with desired skill and understanding of – application of statistical technique based on the data under evaluation. These elements together is beneficial in providing the most insightful, detailed and relevant findings for the study under consideration to our international clients.



As far as the improvement in sales and market share is concerned, just understanding the customer’s need is not sufficient. There is also a requirement of understanding the beliefs of customer for one product or service and that of a competitor as well. While doing this analysis, analysts observe perceptual maps, which are employed to recognize how customers distinguish among products and their perception and comparison with the competitor’s product or service. These maps are helpful for recognizing opportunities to present and position novel products, repositioning current products, and tracking real competitors.

Brand Mapping permits huge data tables’ comparison, for example, product assessments across a range of areas of concern, to be denoted in a two-dimensional map. It condenses enormous amounts of numerical data into an easy to understand, visually attractive layout, using a data reduction methodology which has similarities with old factor analysis. Brand Mapping is so named as it is usually employed to visually denote the scores or ratings of competitor’s brands across a range of benefits. It allows the correlations between specific brands and benefits to be plotted in a collective 2-dimensional space.


The term cluster analysis involves a number of different methods and algorithms to group objects of similar type into related categories. By using this exploratory tool of data analysis, after doing a thorough sorting – different objects having the maximum association is being put in the same group and otherwise in other group. Cluster analysis can be used to determine a specific pattern in data without further explanation or interpretation.



There are a number of diverse approaches that can be utilized to perform an exercise on a cluster analysis. This method could be a Hierarchical method which further is categorized in either agglomerative method or divisive method. Other approach is through non-hierarchical methods. The data used in cluster analysis can be categorical, interval or ordinal. There can be complication, if analysis is being carried out through mixture of different types of variable.



Correlation analysis measures the relationship between two items or two variables. The resulting value is called the “correlation coefficient” displays if changes in one item will effect in changes in the other item. A strong, or high, correlation implies that two or more variables have an excellent relationship with each other, whereas a low correlation indicates that the variables are barely related. Correlation coefficients value can be in the range of -1.00 to +1.00. The value of -1.00 signifies a perfect negative correlation; a negative value implies that all data points lie on a line for which Y decreases as X increases. While a value of +1.00 denotes a perfect positive correlation, a value of +1.00 implies that the relationship between variables X and Y is perfectly linear, with all data points lying on a line for which Y increases and X increases. A value of 0.00 indicates that there is no relationship between the variables which are being tested.


It is data reduction tool or structure detection method which helps in removing redundancy or repetition from a set of correlated variables. This analysis tool represents correlated variables with a smaller set of “derived” variables. It helps in forming and arriving at factors that are relatively independent of one another. With the help of this method, we can simply categorize mainly in two types of “variables”: latent variables/factors and the other one is observed variables.

Factor analysis is being applied in many cases – e.g. through this approach, different groups can be identified which permits us to select one variable that is representing many. It also allows us to obtain insights into categories through identification of underlying factors. It also assists us to categorize people or objects depending on their factor score. Different steps involved in exploratory factor analysis includes – collecting and exploring data and choosing relevant variables from it, followed by extracting initial factors and choosing number of factors to retain. This step is being followed by choosing an estimation method, then rotation and interpretation. On the basis of this step, decision is being made to implement few changes in the form of either dropping an item or including an item. Then the last step is to construct scales and use in further analysis.


Multidimensional scaling (MDS) is a tool through which the level of relationship of individual cases of a dataset can be visualized. It denotes to a set of associated ordination methods employed in data visualization, specifically to show the information confined in a distance matrix. Main objective of an MDS algorithm is to place each object in N-dimensional space such that the between-object distances are conserved as much as possible. Each object is then allocated coordinates in each of the N dimensions. In general, the more dimensions we use to reproduce the distance matrix, the better is the fit of the reproduced matrix to the observed matrix. If we use as many dimensions as there are variables, then the observed distance matrix can be reproduced perfectly. Of course, our objective is to minimize the observed complexity of nature, that is, to explain the distance matrix in terms of fewer underlying dimensions.

The final step of the analysis is the interpretation of dimensions. The actual alignments of the axes from the MDS analysis are random, and can be revolved in any direction. A first step is to generate scatter plots of the objects in the different two-dimensional planes. Three-dimensional solutions can also be demonstrated graphically; but, their analysis is a bit complex. In addition to “meaningful dimensions,” one can also look for clusters of points or particular arrays and configurations.