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What is Descriptive Analysis ?
Descriptive analysis is largely the study of distributions of one variable. This study provides us with profiles of companies, workgroups, persons and other subjects on any of a multiple of characteristics such as size, composition, efficiency, preferences, etc. This sort of analysis may be in respect of one variable (described as one-dimensional analysis), or in respect of two variables (described as bivariate analysis) or in respect of more than two-variables (described as multivariate analysis). In this context it is possible to work out various measures that show the size and shape of a distribution along with the study of measuring relationships between two or more variables.
Descriptive Analysis is in other words called one-dimensional analysis. This analysis shows the benchmark data and measures the stale or condition at any particular time. Descriptive analysis or descriptive statistics are used to present quantitative descriptions in a manageable form. Descriptive statistics helps in simplifying large amounts of data in a sensible way.
Each descriptive statistic reduces lots of data into a simpler summary. Descriptive analysis summarizes the population data by describing what was observed in the sample numerically or graphically. Causal inferences can be drawn through causal analysis of variable. The analysis of variables may be treated one at a time, two at a time, or more than two at a time.
Means, medians, modes, variance, range, and standard deviation typify widely applied descriptive statistics. The level of scale measurement helps the researcher to choose the most appropriate form of statistical analysis. The statistics appropriate for lower order scales (nominal and ordinal) are suitable for higher-order scales (interval and ratio), but the reverse is not true.
The primary goal of descriptive analysis is to gain insights and understand the data without making inferences or drawing conclusions beyond what is observed. It focuses on the "what" rather than the "why" or "how" of the data, providing a factual and objective description of its features.
Definition of Descriptive Analysis
Descriptive analysis is a widely used term, and different authors may provide slightly different definitions based on their specific field or context. Here are descriptive analysis definition by authors:
1) Andy Field, Discovering Statistics Using SPSS:
"Descriptive statistics summarizes and organizes data so they can be easily understood. It is a branch of statistics that describes the basic features of data using measures such as mean, median, and mode."
2) Julie Pallant, SPSS Survival Manual:
"Descriptive analysis is concerned with describing and summarizing the main characteristics of a data set. It involves calculating and presenting summary measures and visual representations to provide a concise overview of the data."
3) Alan Agresti and Christine A. Franklin, Statistics:
"Descriptive analysis refers to the exploration, summarization, and presentation of data to gain insights into its main features. It involves calculating summary statistics, constructing frequency distributions, and generating visualizations to understand the patterns, distributions, and relationships in the data."
4) William Mendenhall, Introduction to Probability and Statistics:
"Descriptive analysis involves the use of statistical measures and visualization techniques to summarize and describe data in a way that is understandable and meaningful. It aims to provide a clear and concise overview of the data and its main characteristics."
5) Daniel T. Larose and Chantal D. Larose, Discovering Knowledge in Data:
"Descriptive analysis is the process of examining and summarizing data to gain an understanding of its key features. It involves calculating summary measures, creating graphical representations, and organizing the data in a manner that facilitates interpretation and communication."
Purpose of Descriptive Analysis
The purpose of a descriptive analysis is to summarize and describe the main characteristics, patterns, and trends within a dataset. It serves several important purposes:
1) Data Summarization:
Descriptive analysis provides a concise summary of complex and voluminous datasets. By calculating summary statistics and organizing data into meaningful formats, it simplifies the data and makes it more manageable for interpretation and communication.
2) Data Exploration:
Descriptive analysis helps in exploring the dataset and gaining a better understanding of its properties. It involves examining the distribution of variables, identifying outliers or anomalies, and detecting patterns or relationships. This exploration aids in generating hypotheses, formulating research questions, or guiding further analysis.
3) Data Presentation:
Descriptive analysis provides a clear and organized presentation of the data. By using visualizations, such as charts, graphs, and tables, it makes it easier to communicate the key findings to a broader audience. This enhances the effectiveness of data-driven discussions, reports, or presentations.
4) Data Quality Assessment:
Descriptive analysis allows for assessing the quality and integrity of the data. It helps identify missing values, outliers, or inconsistencies within the dataset. By detecting data errors or anomalies, it enables data cleaning, validation, and preparation for further analysis.
5) Decision Making:
Descriptive analysis supports data-driven decision making. By providing insights into the characteristics and patterns of the data, it aids in informed decision making. Descriptive analysis can inform decisions related to business strategies, policy formulation, resource allocation, or problem-solving.
6) Benchmarking and Performance Evaluation:
Descriptive analysis enables benchmarking and performance evaluation by comparing different groups, categories, or time periods within the dataset. It helps identify strengths, weaknesses, or areas for improvement. This information is valuable for evaluating performance, setting goals, or making strategic decisions.
7) Understanding Context and Patterns:
Descriptive analysis helps in understanding the context and patterns present within the data. It provides a foundation for understanding the relationships between variables, identifying trends over time, or exploring differences between subgroups. This understanding can contribute to theory development, hypothesis generation, or further investigation.
Types of Descriptive Analysis
Descriptive analysis is used to describe the attributes of a system. A typical procedure is to first identify the attributes present, and then rate these attributes. Magnitude estimation can be used to correlate each of the attribute's relative contribution to the final system. One way to achieve this is to use preset or agreed upon conditions or terminologies. Descriptive analysis is of three types:
1) Univariate Analysis:
Univariate analysis is a statistical method that focuses on examining a single variable at a time. It involves summarizing and analyzing the characteristics and distribution of the variable, such as measures of central tendency, dispersion, and visual representations like histograms or box plots. Univariate analysis is concerned with the description or summarization of individual variables in a given data set.
2) Bivariate Analysis:
Bivariate analysis is a statistical method used to examine the relationship between two variables. It helps to determine whether there is a correlation, association, or dependence between the two variables and to understand the nature and strength of their relationship. Bivariate analysis is concerned with the relationships between pairs of variables (X, Y) in a data set.
3) Multivariate Analysis:
Multivariate techniques are appropriate for analyzing data when there are two or more measurements of each observation and the variables are to be analyzed simultaneously. Multivariate analysis is the analysis of the simultaneous relationships among three or more phenomena.
Techniques for Descriptive Analysis
There are several techniques commonly used in descriptive analysis to summarize and describe data. Here are some of the most commonly used techniques or tools:
1) Measures of Central Tendency:
These measures provide information about the central or typical values of a dataset. The most common measures of central tendency include the mean (average), median (middle value), and mode (most frequently occurring value).
2) Measures of Dispersion:
These measures describe the spread or variability of the data. They provide insights into how much the values in a dataset deviate from the central tendency. Examples of measures of dispersion include the range, standard deviation, and variance.
3) Frequency Distributions:
Frequency distributions organize data into groups or categories and display the number or proportion of observations falling into each group. They are often represented using histograms, bar charts, or pie charts.
4) Percentiles:
Percentiles divide a dataset into hundredths, indicating the percentage of data points that fall below a certain value. They are useful for understanding the distribution of data and identifying extreme values or outliers.
5) Cross-Tabulations:
Cross-tabulations, also known as contingency tables, are used to analyze the relationship between two or more categorical variables. They display the frequencies or proportions of observations in each combination of categories, helping to identify associations and patterns.
6) Summary Statistics:
Summary statistics provide a concise overview of key characteristics of the data. They include measures such as minimum and maximum values, quartiles, skewness, and kurtosis, which give insights into the shape and distribution of the data.
7) Visualization Techniques:
Data visualization techniques, such as scatter plots, box plots, line charts, and heat maps, can effectively represent patterns and relationships in the data. Visualizations help in interpreting and communicating the findings to a wider audience.
8) Correlation Analysis:
Correlation analysis measures the strength and direction of the relationship between two continuous variables. It helps to identify whether variables are positively or negatively related and the degree to which they vary together.
9) Cross-sectional and Time-series Analysis:
Descriptive analysis can be performed on cross-sectional data, which represents a single point in time, or time-series data, which tracks variables over a specific period. Time-series analysis allows for the identification of trends, seasonality, and other temporal patterns.
Advantages of Descriptive Analysis
Descriptive analysis provides valuable insights into the characteristics, patterns, and trends present in a dataset. Here are some advantages of descriptive analysis:
1) Data summarization:
Descriptive analysis allows you to summarize large and complex datasets into meaningful and manageable forms. By calculating summary statistics such as mean, median, mode, standard deviation, and range, you can gain a better understanding of the central tendency, dispersion, and distribution of the data.
2) Data exploration:
Descriptive analysis helps in exploring the dataset by identifying patterns, trends, and relationships between variables. It allows you to visualize the data through charts, graphs, and tables, making it easier to interpret and communicate the findings effectively.
3) Data presentation:
Descriptive analysis provides a concise and organized presentation of the data. It allows you to present key findings and insights in a visually appealing manner, making it easier for stakeholders and decision-makers to grasp the information and make informed decisions.
4) Data quality assessment:
Descriptive analysis helps in assessing the quality of the data by identifying missing values, outliers, and inconsistencies. By examining the distribution of variables and checking for data integrity, you can detect potential errors and take necessary steps to clean and prepare the data for further analysis.
5) Comparisons and benchmarks:
Descriptive analysis allows for comparisons between different groups or categories within the dataset. It enables you to identify differences, similarities, and relationships between variables, which can be useful for benchmarking, performance evaluation, and identifying areas for improvement.
6) Data-driven decision making:
Descriptive analysis provides a solid foundation for data-driven decision making. By objectively summarizing and describing the data, it enables you to derive insights, identify patterns, and make informed decisions based on evidence rather than intuition or guesswork.
7) Communication and reporting:
Descriptive analysis helps in effectively communicating the results of your analysis to a non-technical audience. It simplifies complex information, presents it in a clear and understandable manner, and facilitates data-driven discussions and decision-making processes.
Disadvantages of Descriptive Analysis
While descriptive analysis has its advantages, there are also some potential disadvantages to consider:
1) Lack of Inferential Analysis:
Descriptive analysis focuses on summarizing and describing the data without making any inferences or drawing conclusions beyond what is observed. It does not provide statistical tests or causal explanations for the relationships or patterns identified. Therefore, it may not be sufficient for answering complex research questions or making predictions.
2) Limited Statistical Power:
Descriptive analysis relies on basic summary statistics and visualizations, which may not capture the full complexity of the data. It may overlook important nuances, outliers, or subtle relationships that can be revealed through more advanced statistical techniques. As a result, the analysis may lack statistical power in identifying significant findings.
3) Oversimplification:
Descriptive analysis often simplifies complex data sets into summary measures or visual representations. While this simplification can be useful for communication and understanding, it can also lead to oversimplification and loss of important details. The reduction of data to a few key measures may not fully capture the richness and complexity of the underlying information.
4) Potential for Misinterpretation:
Descriptive analysis relies on human interpretation and judgment. There is a risk of misinterpreting the results or drawing incorrect conclusions, particularly if there is a lack of statistical expertise or a biased interpretation of the data. It is important to exercise caution and critical thinking when analyzing and interpreting descriptive results.
5) Lack of Contextual Information:
Descriptive analysis focuses on the "what" rather than the "why" or "how." It provides information about the data itself but may not offer insights into the underlying causes, mechanisms, or contextual factors that drive the observed patterns. To gain a deeper understanding, additional analyses or complementary research methods may be needed.
6) Potential for Data Overload:
Descriptive analysis can generate a large amount of information and visualizations, especially when dealing with complex datasets. Without proper organization, it can overwhelm the audience and make it challenging to identify the most relevant insights. Clear and concise reporting becomes crucial to avoid information overload.
7) Reliance on Available Data:
Descriptive analysis is limited to the data that is available for analysis. If the data is incomplete, biased, or unrepresentative, the results may be skewed or misleading. Care should be taken to ensure the quality, representativeness, and reliability of the data used in the analysis.
8) Lack of Predictive Power:
Descriptive analysis focuses on describing past or current data, providing little guidance for future predictions or forecasting. It may not be suitable for decision-making processes that require insights into future trends or outcomes.
Descriptive Analysis Examples
Here are some examples of descriptive analysis in different fields:
1) Market Research:
Descriptive analysis is commonly used in market research to understand consumer behavior and preferences. It involves analyzing survey data to determine demographic characteristics, purchase patterns, product preferences, and customer satisfaction levels. Summary statistics, frequency distributions, and visualizations such as bar charts and pie charts are often used to present the findings.
2) Financial Analysis:
Descriptive analysis is used in financial analysis to summarize and describe financial data. It includes calculating key financial ratios such as profitability ratios, liquidity ratios, and solvency ratios to evaluate a company's financial health. Descriptive analysis is also used to analyze stock market data, such as stock price movements, trading volumes, and historical trends.
3) Public Health:
Descriptive analysis is essential in public health to understand disease prevalence, patterns, and risk factors. It involves analyzing epidemiological data to identify the frequency and distribution of diseases in a population, demographic characteristics of affected individuals, and geographical variations. Descriptive analysis is also used to monitor public health indicators, such as vaccination rates, disease outbreaks, and healthcare utilization.
4) Educational Research:
Descriptive analysis is employed in educational research to summarize and analyze educational data. It includes analyzing test scores, attendance records, graduation rates, and demographic information of students to identify trends, achievement gaps, and factors influencing student performance. Descriptive analysis is also used to examine survey data related to teaching practices, learning environments, and student satisfaction.
5) Operations Management:
Descriptive analysis is used in operations management to analyze and improve business processes. It involves analyzing data related to production volumes, cycle times, inventory levels, and quality metrics to identify bottlenecks, inefficiencies, and areas for improvement. Descriptive analysis techniques, such as process mapping, Pareto analysis, and control charts, are commonly used in operations management.
6) Social Sciences:
Descriptive analysis is widely used in social sciences to study social phenomena. For example, in sociology, it can involve analyzing survey data to describe social attitudes, demographic characteristics, and social inequality. In psychology, descriptive analysis can be used to summarize and analyze survey responses, experimental data, or observational data to understand human behavior, cognitive processes, or psychological disorders.