What is Data Interpretation and Report Writing in Marketing Research? MBA Marketing Study Material.

Data Analysis is a very crucial step to fulfill the purpose of research but researcher needs to represent this outcome of analysis in a meaningful way by summarizing it in short sentences, this is the step of Data interpretation and report writing in marketing research. Data Interpretation and report writing is simply the overall conclusion of marketing research. The purpose of research is not well served unless the findings are made known to others.

Data Interpretation:

Data Interpretation is a process of illustrating of inferences from the analysis study in a very meaningful way.  Interpretation is concerned with relationships within the collected data, partially corresponding analysis. Interpretation also covers beyond the data analysis to comprise the results of other research, theory, and hypotheses.

Techniques of Data Interpretation:

Data Interpretation often involves following techniques:

·         Explanation:

The researcher needs to give reasonable explanations of the relations which has found and needs to interpret the lines of relationship in terms of the underlying processes and try to find out the thread of consistency that falls under the surface layer of expanded research findings.

·         Extraneous Information:

During the study, if the researcher has extraneous information which seems unnecessary at that time then it must be considered while interpreting the final results of marketing research.

·         Guidance:

Before boarding upon final interpretation, it is advisable to consult someone having perception into the study who will not vacillate to point out the errors in logical argumentation. This guidance will result in correct interpretation which will enhance the value of research.

·         Relevant Factors:

The researcher needs to finish the task of interpretation only after considering all relevant factors affecting the study to avoid false generalization.

Report Writing:

A report is a page or a document which consist of summery and interpretation of research done based on factual data, opinions of individuals or group. As a matter of fact, even the most intense hypothesis, well-designed research study, and the most outstanding generalizations and findings will have little value unless they are successfully communicated to others. Report writing is the last step in a research study and requires skills beyond earlier steps.

Steps involved in report writing:

·         Logical analysis of the subject-matter:

There are two ways to analyze subject matter logically that are Logical treatment and Chronological treatment. Logical treatment frequently contains in developing the material from the simple possible to the most complex arrangements. Chronological treatment is based on a connection or sequence in time or occurrence. 

·         Preparation of the final outline:

Outlines are the structure upon which long written works are erected. It is an aid to the logical organization of the material and a reminder of the points to be stressed in the report.

·         Preparation of the rough draft:

This step involves the process of analysis of the subject and the preparation of the final outline. The researcher will write down in this rough draft about the procedure adopted of data collection along with its limitations, data analysis techniques adopted in this research etc.

·         Rewriting and polishing:

Usually, this step takes more time than previous steps of report writing. This step involves the careful revision of outline draft revising it very carefully to make it a good piece of writing. The researcher needs to polish the draft by checking the grammar, spellings etc.

·         Preparation of the final bibliography:

A bibliography is the list of books, magazines, and references etc. which are used to study in this research. The bibliography should be organized alphabetically and may be divided into two parts. The first part may cover the names of books and the second part may cover the names of magazine and newspaper articles. 

·         Writing the final draft:

This is the step where the final draft should be written in a brief and objective style and in simple language, avoiding unclear expressions such as “it seems”, “there may be”. A research report should not be dull, should maintain interest and show originality. The report should be an attempt to solve some intellectual problem and must contribute to the solution of a problem and must add to the knowledge of both the researcher and the reader.

Steps involved in this post may vary from research to research, depends on its usage in a study.

What is Data Analysis in Marketing Research and how to deal with it? MBA Marketing study material.

Before going into a conclusion part, Data Analysis is a mainstream process of getting an end result of marketing research. It is an obvious process in which the researcher can reach the final stage of the marketing research. This is time to examine the amount of data and information collected and draw a final conclusion from it.

Once the data has prepared for analysis as shown in the previous post, there are some basic steps which include some statistical procedures. A wide range of collected data should be accurately measured and recorded for analysis to get an accurate conclusion.  Data analysis can be done in two way that are descriptive statistics and inferential statistics. Let’s sort it out one by one:

A.    Descriptive Statistics:

Descriptive statistics is a process of describing and summarising the data in knowledgeable conclusion. This method does not allow a researcher to go beyond the information collected or assumed hypothesis. It simple describe the data to the point as it is. Following are the types of descriptive statistics:

·         Measures of Central Tendency:

Measures of central tendency tools are used to calculate an average value of data or sample. There are three types of widely using averages for research as explained below:

1.   Mean: This is the most popular average type of central tendency. The mean is to be calculated by the sum of all values of data divided by a number of values in data. It is a basic calculation of central tendency.

2.  Median: Median can be calculated by arranging the data in ascending or descending order and selecting a middle value from the arrangement as an average. In an even set of data, the average of two middle numbers can be a Median.

3.   Mode: Most frequently appeared number in data can be a mode average. In a data set, it can be possible that researcher finds more than one mode due to scattered data.

·         Measures of Variability:

Central tendency represents the single value of an average but it cannot describe the data observation fully. Measures of variability can calculate the reliability of the data observation. It also can be used to measure the differences between variable. It is more consistent than a central tendency. Types of measures of variability are:

1.    Range: Range is a basic measure of calculating an average for data set. The range can be calculated by defining a difference between smallest value and largest values from data.

2.    Mean Deviation: Mean Deviation is an average calculated from mean and median of data. It also called for an average deviation. The average can be a mean or median in this type of measure of variability.

3.    Standard Deviation: Standard deviation is very popular in and mostly used a method of getting an average of data. It is a value or an average value calculated from overall data which differ from a mean value.

B.     Inferential Statistics:

In Descriptive statistics, we have seen how to make data analysis with the data or set of data available to us. But in Inferential statistics, we can calculate, measure and analyze the information to check out the reliability and consistency to look into beyond the descriptive statistics. It allows us to compare the group of data and tests hypothesis.

In marketing research, we need the information or data for analysis, but we cannot study the whole population, so we choose a sample size as per a sampling theory. After this process, we need to make some hypothesis. In this case, a sample should be perfect which can represent the whole population. The final stage of inferential statistics is to compare the analysis of data and hypothesis to study the difference between them and make some decision about the research.  

As we can see, Descriptive and Inferential Statistics are two main steps of Data analysis which allows making a decision on research.

Measurement and Scaling techniques in Marketing Research. MBA Marketing Study Material

After getting a knowledge of Marketing research and how to collect data/ information in the previous post, here is the blog on Measurement and Scaling techniques in Marketing Research.

Initially, we have to look into the meaning of Measurement and Scaling in marketing research

Measurement: It is a process of observing, recording and assigning numbers or other symbols to a characteristic of the object according to certain rules.

Scaling: It is a process of assignment of objects to a number. Scaling is developing a continuum/range/series upon which measured objects are located.

In simple words,

There are two forms of data, first comprise of quantitative variables which can be measured in terms of numbers like price, income, expense etc. and second comprise of qualitative variables which cannot be measured in numbers like emotions, feeling, sense, intelligence etc. For further analysis, the organisation needs to convert this qualitative data into the numerical form. Here, measurement and scaling techniques help to convert the data into the measurable form. Here is how:

Measurement and Scaling techniques:

1.      Nominal Scale:

This scaling technique is basic and simple to understand. It is a technique of assigning labels or numbers to the variables. In this type, there is no existence of numerical significance. There is no link between the variables and numbers or labels allocate to them. It can be called as a “Label Scaling technique”.

e.g.

Gender-    1. Male   2. Female

Marital Status-    A. Married   B. Unmarried

2.      Ordinal Scale:

Ordinal scaling is a process of ranking the objectives according to their characteristics or features. Ordinal scale helps to convert the data from unmeasurable to measurable one, so that researcher can analyze the particular value of responses and rank it as per its feature. This scale is basically used to measure non-numerical concepts like emotions, satisfaction, intelligence etc.

e.g.

How would you rank our product? -   1.Satisfied   2.Unsatisfied   3.Delighted

3.      Interval Scale:

Interval scale can be measured an absolute value or difference of values between scale points. This scale not only can rank the data but also can convert the difference in numerical form so that researcher can find correct figure to record it properly. This type of scale gives a strong figure for non-numerical responses in numbers.

e.g.

Rate our performance on a 0-10 scale- The answers can be 8.2, 9.2. 1.3, 5.6.3, 4, 6.6 etc.

4.      Ratio Scale

The ratio scale is the ultimate level of scale by its use as it allows the researcher to find or classify the object, rank the object as well as compares the difference between the intervals and gives the result in ratio form. In short, Ratio scale includes nominal, ordinal as well as interval scale pattern to measure and record the data in sequence.  It compares both distinctions in rating.

e.g.

What is the average temperature measured in the USA in last three months?-

In the first month- 20 degree- 2^nd rank

In the second month- 22.5 degree- 3^rd rank

In the third month- 10 degree- 1^st rank

Above explained techniques are the basic in measurement and scaling the research which can be used on collected data through Questionnaire as described in the previous post.