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.

What is Sampling in Marketing Research? MBA Marketing study material.

In Marketing Research, recognize and select the sample is very crucial part to collect information. This article helps to understand what is sampling? And How to identify and select a correct sample size?

What is sampling?

It’s a process of identifying and selecting a number of units from a whole population for our marketing research. Sample is a subgroup of larger population of individuals or objects.

In short, it is a process of selecting small amount of people from whole large population for study. It is more feasible, less time consuming and cost-effective to analyse or study of smaller amount of individuals rather than to study large population. Sample size is a group of an individuals for whom the questionnaire is to be designed aims marketing research.

Sampling Methods:

Generally, sampling is classified into two groups that are probability sampling and non-probability sampling. Let’s have a look how it work:

1.      Probability sampling:

In this method, every object or an individuals has an equal chance of getting selected for research. This method of sampling have many types as follows:

• Simple Random sampling: This is a basic type of sampling in which every individual or object has an equal probability to get selected. Lottery method is the best example of Simple random sampling. This type is simplest, cheapest and fastest but unreliable in some points because of its uneven population.

• Systematic Random sampling: It is modification of simple random sampling. In systematic random sampling, Object or an individual is to get selected by calculating desired sampling fraction. E.g. Every 5^th person or object, every 15 mins, every 15 houses, every 2 kilometers, every 3^rd shop etc.

• Stratified Random sampling: In this type of sampling, Whole population is divide into strata according to its characteristics and then sample is going to select randomly. In this type, stratification can take place even of selecting an Individuals. Stratification of population can be done by age group of people, income of people, and number of dependents accordingly.

• Cluster Random sampling: ‘Cluster’ is the name itself shows the meaning of this type of sampling. It is a process of forming a group or bunch of an individuals or object according to their features and within this group we can choose a set of sample randomly. E.g. there are 2000 schools in one town for research, if we apply this method, we cluster the geographic area of town where each area has 50 schools and within 50 schools we have to select every 10^th school as our sample set.

2.      Non-probability sampling:

As the name stated, non-probability sampling is a process of identifying and selecting sample set not by randomly but as a subjective.  It is much less formal than probability sampling method. It involves:

• Convenience sampling: This non-probability sampling method is used to select set of sample from those who willing to involve in research or who are volunteers to get selected.

• Judgement sampling: Judgement sampling is exactly opposite of simple random sampling. This method involve the process of selecting sample by own personal judgement and opinion.

• Quota sampling: It is a process of selecting a representative sample by dividing whole population into quotas according to their variables like age, income, location etc. Sample is getting selected by drawing its quota from each variables.

• Snowball sampling: In this method, selected sample size gives the references from their personal network with same features and characteristics. This method is supportive for organisation to get additional samples for its research.