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.

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.