Data Processing and Preliminary Data Analysis in Marketing Research. MBA Marketing study material

Data Processing and Preliminary Data Analysis is a point of doing marketing research at first place in order to collect raw data and transform it into a knowledgeable form for analysis and interpret the final result. Initially, after designing a suitable Questionnaire, researcher need to take a field survey to collect the data. This blog comprise the practice of transformation of data into a logical and expressive content.

Data Processing:

Data processing simply the process of produce a data that can be convertible into knowledge. Data processing is a first step of final marketing research. After taking hundreds and thousands of survey, researcher need to check out the data that can be convert into some meaningful form. There are several steps involved in this process as below:

·         Questionnaire examination:

Researcher need to check the question formation, incorrect questions, rejection of unacceptable questions, removal of incomplete questions and also need to check past cut-offs date or missing pages. 

·         Editing:

Editing is a process of generate data which is correct and accurate. It corrects the errors detected from the data where ever possible. The purpose of editing is to ensure that each questionnaire is accurately completed. It confirms that the information collected from questionnaire is error free and ready to transfer into final analysis.

·         Coding:

Coding is nothing but assignment of numbers, symbols, or alpha to various data categories through some measurement and scaling techniques. Some information need to take into a featured category and so it need to record it by its number or symbol. Coding is a process of ensuring every category is in numerical form accurately to push into final analysis. It is usually applied on open-ended questions.

·         Transcribing:

Transcribing is entering a data into various software that can be accessible to people. Data entry software in this process should be user friendly to easily reachable to people. There are various methods and statistical formats for entering data easily.

·         Cleaning:

While doing a transcribing, cleaning of raw data is very important step of data processing. Raw data may have defective logins, number of errors which need to fix it with the step of entering data. Purpose of cleaning process is to make sure the consistency of the data which is transcribed.

·         Statistical Adjustment:

Some data need to represent visually by graph or statistical chart to show the result more effectively. Statistical adjustment is a step to ensure the graphs, charts or tables involves in data are correctly entered. Researcher also need to check the graph or chart are exactly placed on assigned location. 

Researcher can take these above steps according to the need of research.

Preliminary Data Analysis:

Preliminary data analysis is the first stage of research which mostly concerned with descriptive statistics. Descriptive statistics is the process of describing the characteristics of primary data which provides initial analysis of assumptions. This is important process for detecting any violation of assumptions in data. It gives a clear vision to researcher as to where the violation occurs. It embraces some statistical and arithmetical terms like Mean, standard deviation, range of score, skewness etc.

Nowadays, SPSS (Statistical Product and Service Solutions, SPSS Statistics) is a commonly used statistical analysis software package as well as SAS, Stata, Minitab are also widespread for descriptive statistical analysis.

In usual preliminary research process, there are three mainstream stages:

1.      Exploratory Analysis: This analysis shows a limitations to the primary data analysis and convert it into an evidence. It helps to resolve the difficulties by taking questions as a proof for final result.

2.      Developing the findings: It is a process of clean the data set from violations and generate the summery, relationship, narratives, and interpretations also recommendations for research users.

3.      Archiving: In archiving stage, data processor can keep the record of non-transient data for further analysis. Due to vast and complicated process of research analysis, researcher can only take fraction of content. So this step helps to save other inconsequential sets of data.

Hope this blog helps you to understand Primary stage of marketing research that is data processing and preliminary data analysis.

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.