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Data collection is a hefty task . It’s not always that a researcher or even a skilled analytics professional is able to establish a relation between the relevant variables to bring out the exact information that helps the user in making informed decisions. This is particularly cumbersome when there is a large amount of data involved. It is imperative that the right analytical tools are chosen so as to extract meaningful conclusions from scattered data.
Cross tabulation or contingency table is a largely used data analytics tool that helps break down complex data to derive meaningful relationships between multiple variables. This relationship is then used to establish trends , make assumptions and study how change in one parameter affects the other.
Such an analysis helps in scoping down on what’s important while eliminating the unnecessary figures that have no value addition. This filtering process eliminates the irrelevant aspects of a study allowing users to focus on important characteristics and their inter-connection with each other.
Let’s understand this with an example . Think about the things that you have to factor in while considering buying a house . The basics of number rooms , modular kitchens , tiling , lighting , amenities among other things are your priority. Apart from that you also have to look at other aspects such as proximity to markets and playgrounds, the area in which it is located , separate parking space , sanitation facilities and of course , the price and payment options. Keeping all of this in mind , you arrive at a decision to buy or not to buy the house after consulting with your family members.
So basically what you do is interrelate independent variables that affect your buying decision , weigh your options , consult others and arrive upon a decision well though over. This is basically what cross tabulation does for a set of data. It breaks down huge chunks of variables that influence important business decisions to establish logical relationships.
Cross tabbing is great analytical for arranging your data in a systematized manner that guides deeper understanding by establishing correlation between inclusive variables.
The importance of such a tool should not be undermined :
Cross tabulation categorizes scattered chunks of data to make it manageable.
When huge amounts of data is involved , it is usually daunting to look for key areas that guide organizational decisions. Data gets mixed up and making sense out of it becomes like straightening mashed up wires.
Cross tabulation puts data into perspective by establishing connections between the variables that the users wish to focus on. These connections are then used to make inferences and study the market through a narrow point of view. This makes it easier to identify patterns which are then used to make inferences about market standards and preferred practices that can provide an impetus to targeted strategies.
Researching a particular topic might bring in additional data points that may not be of any value to the end user. These data points make the data complex and hide useful areas that need to separated to make the data easier to handle.
Cross tabulation sub-divides the data into comprehensible data sets. These data sets are the converted into a table to establish to gauge their inter-relationship with each other. This highlights the material data against the add ons that might not make any value addition to the organization making use of it. It is a filtering process that lies at the core of generating manageable statistics that channelize the decision making process in the right direction.
It is difficult to spot specific insights and quantities when they are sprinkled. Through particulate organization, deep insights gets uncovered from raw data that make the data actionable. By projecting relationships , organizations get a hint of exact pain points that need to be targeted for effective and quick results.
Such an assortment is a tedious task without cross tabulation or when done manually. Establishing cross sectional quantitative relationships provides a substantial backing to all decisions and helps in mustering stakeholder confidence.
Cross tabulation is a fairly easy process that is compatible with all data types : Ordinal , Cardinal and Nominal . This provides a wide array of data on which cross tabulation can be applied.
The analysis done using cross tabulation is easy to grasp even when the user has no high level statistical knowledge. Stakeholders wishing to use cross tabulation for decision making can highly rely on it as a useful measure that prevents confusion and ambiguity while evaluating hard data.
A chi-square test is a statistical model that compares a data model with actual observed data. It is a technique of hypothesis testing that requires the independent inputs from a large enough sample. Larger a sample, the more likely it is to generate realistic results. Chi-square highlights the deviation between actual and a model data set to see the applicability of that model. This provides additional surety to the end user of the usability of a particular data set.
Chi-square is robustly used as a verification mechanism along with cross tabulation. A relationship assessment chi-square test tells if the variables under study share a relationship or not. The chi-square test is a statistical procedure for testing the goodness of fit of two categorical variables, such as gender and country. It will help you to find out if there is a relationship between the two variables, and if so, the strength of that relationship.
Chi-square assesses if there is a null hypothesis i.e. there is no relationship or the presence of an alternative relationship i.e. a relationship exists. The null hypothesis only gets rejected if the probability of the relationship existing by chance( or p) is less than or equal to 0.05 . If this value stands to be greater than 0.05 , it is an indication of the findings being substantial.
Let’s take an example : Given below is the cross tabulation of a random sample of men and women who were picked out to express their choice of reading genres.
It is a fairly basic representation of the relationship between gender and their choice of readings. The quantum of relationship is determined by whether the two variables in consideration are independent or not. If so , the variation in one values of the variable is likely to have no impact on the other variable and vice versa. Upon calculating the chi-square statistic value for the above table , the following results are derived:
Focus on the p-value = 0.000680694 as against the Alpha value of 0.05 . Since the p-value for the given cross table is less than 0.05 , the null hypothesis gets rejected which indicates the existence of a significant relationship between gender and reading preferences as the alternative hypothesis.
Cross tabulation can be used widely to assess market data on any aspect , break down complex categories and establish trends and patterns that can be of great assistance to each level in an organization.
Customer satisfaction based on organizational service can be gauged deeply using cross tabulation. Customer satisfaction surveys measure NPS , CSAT and CES among others to evaluate how customers feel about the company. These satisfaction levels can be analysed in terms of the variation that exists with respect to age , gender , location, products purchased and many other such segments. This provides a narrow view of the customer’s level of content to identify pain points that need to be worked upon to serve certain segments better
Cross tabulation can be used to analyse market sentiment towards company offerings. Studying areas such as : why certain company products have greater demand, what particular attributes do customers look for and how these attributes vary across customer segments , competitive performance of other companies offering the same products and differences between the products offered by multiple players in the market. All of this becomes easier to pin point as a result of cross tabulation. The assessment on these lines can help minimize risk when used for concept testing which allows the companies to make their offerings market ready before the actual launch.
Your customers are as happy as your employees. Regular assessment of employee feedback , inputs , engagement and satisfaction levels helps boost employee motivation and create a positive work environment. Employee surveys can be used to gather viewpoints about company concepts and offerings, address internal grievances and queries and allow employees to present ideas and inputs to improve general functioning and performance. Such data when interpreted using cross tabs , gives an outlay of the employee feelings towards the internal management and satisfaction levels. Doing this provides a platform for employees to express their authentic ideas and suggestions as well as resolve internal issues by focusing on important areas that need immediate addressal.