Quantitative Data Analysis

Data collection forms a major part of the research process but this data has to be analyzed to make sense of it. Here we take a look at the various methods to analyze quantitative data.

Virtually any business and management research you undertake is likely to involve some numerical data or contain data that has or could be quantified to help you answer your research question(s) and to meet your objectives. Quantitative data refers to all such primary and secondary data and can range from simple counts such as the frequency of occurrences of an advertising slogan to more complex data such as test scores, prices or rental costs.

However, to be useful these data need to be analysed and interpreted. Quantitative analysis techniques assist you in this process. They range from creating simple tables or graphs that show the frequency of occurrence and using statistics such as indices to enable comparisons, through establishing statistical relationships between variables, to complex statistical modelling.

Before you begin to analyse data quantitatively you should ensure that your data is already quantified or that they are quantifiable and can be transformed into quantitative data , that is data which can be recorded as numbers and analysed quantitatively. This means that prior to undertaking your analysis, you may need to classify other forms of data (such as text, voice and visual) into sets or categories giving each category a numerical code.

Related: Understand the complete Research Process

Steps to conduct Quantitative Data Analysis

For Quantitative Data, raw information has to be presented in meaningful manner using analysis methods. Quantitative data should be analysed in order to find evidential data that would help in the research process.

Relate measurement scales with variables:

Associate measurement scales such as Nominal, Ordinal, Interval and Ratio with the variables. This step is important to arrange the data in proper order. Data can be entered into an excel sheet to organize it in a specific format.

Connect descriptive statistics with data:

Link descriptive statistics to encapsulate available data. It can be difficult to establish a pattern in the raw data. Some widely used descriptive statistics are:

1. Mean- An average of values for a specific variable
2. Median- A midpoint of the value scale for a variable
3. Mode- For a variable, the most common value
4. Frequency- Number of times a particular value is observed in the scale
5. Minimum and Maximum Values- Lowest and highest values for a scale
6. Percentages- Format to express scores and set of values for variables

Decide a measurement scale: It is important to decide the measurement scale to conclude a descriptive statistics for the variable. For instance, a nominal variable score will never have a mean or median and so the descriptive statistics will correspondingly vary. Descriptive statistics suffice in situations where the results are not to be generalized to the population.

Select appropriate tables to represent data and analyze collected data: After deciding on a suitable measurement scale, researchers can use a tabular format to represent data. This data can be analyzed using various techniques.

Quantitative Data: Analysis Methods

Here are varioous methods that are commonly used to analyze quantitative data collected in surveys.

Cross-tabulation: Cross-tabulation is the most widely used quantitative data analysis methods. It is a preferred method since it uses a basic tabular form to draw inferences between different data-sets in the researchLinks to an external site. study. It contains data that is mutually exclusive or have some connection with each other.

Trend analysis: Trend analysis is a statistical analysis method that provides the ability to look at quantitative data that has been collected over a long period of time. This data analysis method helps collect feedback about data changes over time and if aims to understand the change in variables considering one variable remains unchanged.

MaxDiff analysis: The MaxDiff analysis is a quantitative data analysis method that is used to gauge customer preferences for a purchase and what parameters rank higher than the others in this process. In a simplistic form, this method is also called the “best-worst” method. This method is very similar to conjoint analysis but is much easier to implement and can be interchangeably used.

Conjoint analysis: Like in the above method, conjoint analysis is a similar quantitative data analysis method that analyzes parameters behind a purchasing decision. This method possesses the ability to collect and analyze advanced metrics which provide an in-depth insight into purchasing decisions as well as the parameters that rank the most important.

TURF analysis: TURF analysis or Total Unduplicated Reach and Frequency Analysis, is a quantitative data analysis methodology that assesses the total market reach of a product or service or a mix of both. This method is used by organizations to understand the frequency and the avenues at which their messaging reaches customers and prospective customers which helps them tweak their go-to-market strategies.

Gap analysis: Gap analysis uses a side-by-side matrixLinks to an external site. to depict quantitative data that helps measure the difference between expected performance and actual performance. This data analysis helps measure gaps in performance and the things that are required to be done to bridge this gap.

SWOT analysis: SWOT analysis is a quantitative data analysis methods that assigns numerical values to indicate strength, weaknesses, opportunities and threats of an organization or product or service which in turn provides a holistic picture about competition. This method helps to create effective business strategies.

Text analysis: Text analysis is an advanced statistical method where intelligent tools make sense of and quantify or fashion qualitativeLinks to an external site. and open-ended dataLinks to an external site. into easily understandable data. This method is used when the raw survey data is unstructured but has to be brought into a structure that makes sense.