Tools and techniques for qualitative and quantitative analysis and how it impacts your business

Statistical analysis plays a vital role in devising strategies for a successful business. Almost all organizations use statistical methods to analyze their business data, which results in improved production, sales, and customer satisfaction. There are two types of Statistical analysis, i.e., quantitative analysis and qualitative analysis. This article will discuss these techniques and their use in enhancing business productivity and sales in detail.

1. Qualitative Analysis

Qualitative analysis is performed on qualitative data. Thematic analysis is the most widely used method of qualitative data analysis. In this method, companies collect information from their customers through semi-structured interviews and open-ended surveys. Companies usually design questions about their interest, such as product/ service quality, the requirement of new features, customer satisfaction, etc. After collecting the data, text transcripts are studied line by line, and meaningful words convey the customer’s core thoughts are underlined. These meaningful words are called codes. After the identification of codes, patterns are identified among them, and themes are created. A theme may contain many codes. After this step, the frequency of codes is analyzed, the codes with low frequency are discarded. Based on these themes, decisions are made.

Nowadays, there exist tools, which can be effectively used to analyze qualitative data. Following are some of the popular tools for thematic analysis:

• NVivo
• QDA Miner Lite
• Max QDA
• Quirkos
• Qualtrics

2. Quantitative Analysis

Quantitative analysis is the most widely used analysis technique to find insights into business data. It is done based on quantitative data, which is collected through closed-ended questionnaires/ surveys. It is easier than qualitative analysis. The quantitative data contains various types of data such as nominal scale, ordinal scale, interval scale, and ratio scale. Data cleaning is the first step after the data collection, as collected data might have missing values. Various techniques are used to fill the missing values such as mean/ median imputation, mode imputation, regression imputation, etc. Mean/ median and mode imputation are the most widely used techniques. Mean/ median imputation is used to fill missing numerical data values (includes both discrete and continuous data). In comparison, mode imputation is used to fill missing values of categorical data.

After filling the missing values with a suitable technique, we apply two types of statistics to the dataset. These two techniques are 1. Descriptive statistics and 2. Inferential statistics.

Credit: https://www.mymarketresearchmethods.com/

1. Descriptive Statistics

Descriptive statistics tell us about the data distribution, which includes the meaning, median, mode, range, minimum, maximum, standard deviation, variance, skewness, kurtosis, quartiles, and percentiles. Various types of graphs are also used to find the dataset distribution, such as histogram the boxplot. The histogram shows the normality of the data graphically, while the boxplot illustrates the minimum, maximum, quartiles, mean of the data and identifies outliers graphically. For instance, we may know about the number of customers (knowing minimum, maximum, and average value) interested in a particular product/ service. Several customers satisfied with the service, sales trend in a particular period can also be identified, which may play an essential role in enhancing productivity, customer satisfaction, and revenue generation.

2. Inferential Statistics

Inferential statistics is used for hypothesis testing. Popular methods of inferential statistics include t-test, Analysis of Variance (ANOVA), and chi-square test. The t-test is used to compare the means of the two groups. For instance, one group of customers shows their interest in a particular product/ service, and other groups of customers express their interest in some other product/ service. The t-test will compare the means of customer interest, and based on this information, the hypothesis will be accepted or rejected, and weak areas will be improved. The chi-square test is used to find the relationship between two categorical variables. For instance, it can be used to know the association between customers’ interest (based on gender, i.e., males and females) and the type of product/ service. ANOVA is used to know whether the difference between two or more unrelated groups is statistically significant or not.

Following are some of the popular tools for quantitative analysis:

• IBM SPSS
• R Language
• SOFA
• SAS
• Stata
• JASP

Conclusion

Both analysis techniques are equally effective, but quantitative analysis is more popular than the industry. If you want to build your career as a statistical analyst, you must learn all the above discussion tools and techniques.

References

• https://study.sagepub.com/oleary3e/student-resources/analysing-data/steps-in-quantitative-analysis
• https://www.scribbr.com/statistics/t-test/#:~:text=A%20t%2Dtest%20is%20a,are%20different%20from%20one%20another
• https://www.geeksforgeeks.org/difference-between-descriptive-and-inferential-statistics
• https://www.chegg.com/learn/statistics/introduction-to-statistics/inferential-statistics
• Cover photo credit: Fintellspot.com