Research paradigms and their alignment with data collection methodology

Research Project Considerations Researchers must evaluate the philosophical assumptions they hold and those underpinning the research questions before choosing data collection methods. These philosophical assumptions are related to the real nature, ontology, and the nature of knowledge, also called epistemology. These two ideas form the worldview of research. Therefore, researchers need to align the researcher’s worldview with the methodology or the approach to study and the specific tools for data collection and analysis. These are key in determining the quality of a research project (Silverman, 2020).
The main research paradigms in the field of management and research Positivist Paradigm This research method aims to search for the research information from an exclusive viewpoint without references to any other issue. With this paradigm, the only user information is the primary observable information. Referencing the work of other scholars is not done. The method primarily used for data collection is qualitative. The data obtained is highly organized and has various samples (Antwi & Hamza, 2015).

Interpretivist Paradigm This is a method of qualitative primarily used in social sciences that places the main focus on human beings and the ways they use to interpret reality. This research mainly aims to explore and investigate new idea r ideas without necessarily using many examples. This type of research is bound with the realism paradigm even though the conclusions are different.

Realism Paradigm This research method aims to investigate the truth by any possible means while keeping a sense of humility to the conclusions. Much attention is paid to the information and data obtained from the works of other scholars (Antwi & Hamza, 2015). Attention is also paid to the world and cultural views. As mentioned earlier, interpretivism and realism paradigms are bound together but with different conclusions.

Pragmatism Paradigm This is social constructivism and has a primarily ideographic research mode and a mainly observable source of knowledge. This paradigm highly needs complex and quantitative management as far as the information system is concerned. The primary method applied under this research paradigm is a practice that helps with the interpretation of data.

Positivist and Interpretive Research Paradigms Motivation is critical in the research process. It drives the research process, ensuring that the research is conducted following the prior decisions. While using either the interpretivism or the positivist research paradigm, the researcher is motivated differently owing to the differences in the focal point of the research. Using the positivist paradigm, the researcher needs to understand and explain what has been considered from the world and eventually share the results. The interpretive paradigm, on the other hand, applies the world’s understanding as a method. However, the interpretation of the phenomena is made from the researcher’s point of view, after which the knowledge is shared with others. Hence, identifying a work motivation coupled with different paradigms leads to different results (Antwi & Hamza, 2015).
The difference between quantitative and qualitative research and circumstances where each is appropriate: Qualitative Research and Appropriate Circumstances This is also known as market research. This method of analysis emphasizes collecting data through conversational and open-ended communication. It covers the “what” and the “why” of a research question from the source of information. This type of research is based on the disciplines of the social sciences, including sociology, anthropology, and psychology. As a result, the methods of qualitative research are designed to allow for deep and further probing.
The questions directed to the respondents are also based on the responses they give to help the researcher or the interview to understand the respondent’s feelings and motivation better. This is essential because understanding the principles, guidelines, or determinants of the decisions taken by the audience helps when concluding research. This research method is exploratory and helps uncover the trends in opinions and thoughts. It depends on the real experiences of people as agents in their daily lives (Silverman, 2020).
The data obtained using this method is non-numerical and instead focused on people’s behavior from the respondent’s viewpoint. Researchers look at knowledge as subjective and not objective, and this assumption is used as the guiding principle when conducting qualitative research. Therefore, the researcher learns best and obtains relevant information used to derive the conclusion from the participant.
Qualitative research can be used in various situations, including when generating and developing a new product when there is a need to understand the dynamics of a decision or decisions about purchases, and when there is a need to investigate the marketing and position strategies of a current or a potential brand/ service or product. This research method can also be used when determining the weaknesses and the strengths of a brand or product (Silverman, 2020). It can also explore market segments, demographics, and customer groups and study and understand the audience’s reactions regarding marketing campaigns. Quantitative Research and Appropriate Circumstances This is a systematic investigation of a research topic or question obtained by obtaining quantifiable data followed by statistical, computational, and mathematical analysis. Information or data is collected from pre-existing sources and other potential customers. Sampling methods are used for the data collection procedure coupled with other techniques like online polls, online surveys, and questionnaires, among other tools. The research results are represented in a numerical form, after which critical analysis is carried out. Once an understanding of the numbers obtained is done, the results can forecast the future of a given service or product. Where necessary, appropriate changes are made. It is primarily done in the social sciences by using statistical methods to collect quantifiable data. The results that are obtained are statistical, unbiased, and logical. Data is collected using highly structured processes and tools and is done on a sample that serves as the whole population’s representative. This research method is data-oriented (Watson, 2015).
This research method is most appropriate when quantifying attitudes, opinions, and behavior and also helpful when quantifying other variables and making generalizations from a given population. The quantifiable data is used in articulating facts and revealing the patterns followed by research.
The two research methods generally differ in the forms of data produced (descriptive versus numerical), flexibility degree, questions asked (open-ended versus closed-ended), data collection tools, and the objectives of the analysis (Watson, 2015). Qualitative Data Gathering Techniques in Business Research Qualitative research techniques are designed in a way that helps to reveal the perception and behavior of the audience concerning a specific topic. The results obtained from this research method are descriptive, and the conclusion can be drawn quickly from the data obtained. The forms originate from behavioral and social sciences. The following are the techniques frequently used.

One-on-One Interviews This is perhaps the most popular technique of qualitative research. The in-depth and personal interviews are a great way of obtaining first-hand information from the respondents. A respondent takes part in a personal interview at a time. The main advantage of this technique is that it is purely conversational; hence, a researcher or interviewer can get in-depth and detailed information from the respondent. With this technique, the researcher gathers precise data or information about the people’s beliefs and motivations. Having a well-experienced researcher increases the chances of asking the right questions, leading to collecting meaningful data. The researcher can also ask follow-up questions to help with the collection of more relevant information. The interviews can be done on the phone or face-to-face. A face-to-face interview allows the researcher to read the respondent’s body language and align them with the responses they give (Sukamolson, 2007).

Focus Groups This is also a popular technique used to carry out data collection in qualitative research. A focus group comprises a small number of respondents, usually between 6 and 10, obtained from the target market. Their aim is usually to answer the research questions “why,” “what,” and “how.” The main advantage of this technique is that the researcher does not necessarily have to interact with the group members in person. With the current technological advancements, the responses from the focus groups can be obtained by simply clicking a button. An online survey can be quickly sent to various devices from where they access the questions. However, this is an expensive approach and is mainly used only when explaining complex processes (Sukamolson, 2007). On the bright side, this technique is convenient and suitable when researching a new brand or product or to test new ideas and concepts.

Ethnographic Research This is generally the most in-depth observation method used to study individuals in their natural environment. This technique mainly focuses on understanding the motivations, challenges, and cultural settings occurring in a particular environment. With this technique, a researcher gets a first-hand feel or experience of the natural setting, rather than relying on discussions and interviews. It can take place for a few days or last up to several years since it involves data collection and in-depth observation on particular grounds. (Luna?Reyes & Andersen, 2003). However, this method can be time-consuming and challenging and entirely dependent on the researcher’s expertise in observation, analysis, and inference of the collected data.

Case-Study Research This method or technique has gone over evolution over the years to become a valuable research technique eventually. This method of qualitative research is used to explain a whole organization. Its operation might look complicated, seeing that it involves a larger sample, but it is perhaps the simplest method of conducting research. This is because it involves a thorough and in-depth and in-depth understanding of the methods used to collect data and draw conclusions.

Use of Records With this technique, the researcher will use pre-existing documents that are reliable and have similar information as the source of data. The data obtained from this method of qualitative research can further be used to conduct new research. This method is identical to getting data from books or sources in a library. The researcher goes over books and other material as references and collects information or data relevant and is likely to be used in the research.

Observation Process This research process takes advantage of subjective methodologies in gathering data or systematic information since this is the main focus of qualitative observation. Qualitative observation is mainly used to determine a meeting point of the quality differences. It also deals with the significant sensory organs and how they function, namely smell, sight, hearing, touch, and taste. It does not involve numbers or measurements. Instead, it involves characteristics or traits. For instance, observing the customer’s shopping behavior will enable a researcher to watch how the consumers react to the products. This also allows for collecting first-hand information, rather than using the statement or answers they would have provided on a written review (Sukamolson, 2007).

Real-World Dialogue and Lifestyle Immersion Lifestyle immersion is where a researcher attends a live event like a gathering or a party and gets an uninterrupted view of the consumer’s behaviors and attitudes. During such activities, the researcher observes the consumers have dialogues. As the researcher listens to honest conversations, they have a powerful way to understand the consumers’ frustrations, motivations, and desires. This technique is essential when conducting business research (Lochrie, Curran &O’Gorman, 2015).

Quantitative Data Gathering Techniques in Business Research Qualitative research methods of data collection are broadly categorized into two; primary quantitative research techniques and secondary quantitative research techniques. Primary quantitative research data collection techniques include sampling methods, surveys and polls, and different question types (Taheri et al., 2015). Examples of secondary research techniques are data from the internet, non-government and government sources, obtaining data from educational institutions, public libraries, and sources of commercial information. These are briefly described below.

Sampling Method Two sampling methods are generally used for data collection; non-probability and probability sampling. With probability sampling, the researcher uses the theory of probability to filter individual data from the general population data, after which probability sampling samples are created. For a particular sample, the participants are selected randomly to avoid issues of bias. Each participant of a sample stands an equal chance of being selected. The types of probability sampling include stratified and simple random sampling, systematic, and cluster sampling (Taheri et al., 2015).
With non-probability sampling, the researcher uses his or her experience and knowledge to create a sample. This method is a little biased because the researcher is involved; therefore, participants do not stand equal prospects of being chosen for the sample. There are different models under this sampling method: consecutive, convenience, quota, judgemental, and snowball sampling. Data Collection Using Polls and Surveys The sample is pre-determined, after which polls or surveys are distributed to collect the relevant data to be used for the quantitative research.
A survey is a research technique used for data collection from a pre-determined group of individuals or respondents to gather information and have insights on various topics a researcher is interested in. The ease of distribution and the number of people the survey reaches are highly dependent on the amount of time the research is supposed to take, coupled with the study’s objectives. These are the most fundamental aspects of quantitative analysis and also determines the outcome of the study. Four scales of measurement are used to create the multiple-choice questions to be used in a survey. These include; ordinal, interval, ratio, and nominal measurement scales. These are essential since, without them, it is impossible to create a list of multiple-choice questions. Also, to conduct quantitative research, close-ended questions are used and can include multiple questions and rating scale questions (Taheri et al., 2015).
Polls are also an important data collection method and are extremely useful in providing feedback when coupled with close-ended questions. The primarily used poll types include the exits and election polls. Such polls collect data from a large population or sample size using basic questions like multiple-choice questions. Using Data Available on the Internet With the current technological innovations and advancements, it has become even easier to conduct quantitative research owing to the higher mobile devices’ penetration and the internet. It has become easy to obtain data about any particular research topic online. This helps in boosting data validity and also offers the relevance of data collected previously.
Non-government and government sources
These sources can be crucial when conducting secondary quantitative research. These include information obtained from various reports on market research. The data obtained using this method is highly in-depth and reliable and can also increase the research design validity (Taheri et al., 2015).
Public libraries
This has become a sparing method of quantitative research but still serves as a reliable information source. They have a variety of copies of research that have been conducted earlier and documented. Public libraries act as a store for valuable documents and information. Researchers can extract information that is relevant to their research from such sources.
Educational institutions
The majority of educational institutions conduct valuable research on various topics and are a rich source of information regarding specific research topics. They also publish reports of the research they do for documentation purposes, which can be a crucial source of validation.
Commercial sources of information
These comprise but are not limited to journals, local newspapers, radio, television, and magazines and can provide crucial information or data relevant to the research. These sources have in-depth and first-hand information on various topics like political agenda, economic development, and market research.
Data analysis techniques in qualitative business research
Because of its nature, the analysis and interpretation of qualitative data can be time-consuming and challenging. After data collection, a researcher is likely to have numerous texts or long audios to go through. Compiling every data obtained and making an inference can, therefore, be a lot of work. The following are various analysis techniques that researchers can use to analyze data from qualitative research.
Qualitative content analysis
This is possibly the most straightforward and standard method of analyzing qualitative data. This is a method employed to evaluate patterns in content like phrases, images, or words. It also evaluates practices cutting across various pieces of content like a newspaper article collection. With this analysis method, the researcher identifies the frequency of an idea or the patterns of underlying interpretations—this method of data is used in numerous ways. Therefore, the researcher’s responsibility is to develop a specific question or objective that will serve as the data interpretation and analysis process (Kawulich, 2004). Large amounts of information are grouped in smaller chunks, and every category is summarized. However, it can be time-consuming.
Narrative analysis
This is about listening to what the respondent has to say and analyzing the information. This analysis method can discover the “how” of an idea or concept and why it is essential. An analysis of how an entrepreneur is talking about their career can provide meaningful insight into how they think and their perception. With this method, the researcher has to pay attention to the details of the information provided by the respondent. Generally, the sample sizes are smaller compared to qualitative content analysis.
Discourse analysis
This describes the spoken or written debate or language. With this method, language is analyzed in the social context. This means that an aspect of languages like a speech or conversation is analyzed within the society and culture it happens in, for instance, exploring how an employer talks to their employees. This type of analysis allows one to identify how history or culture influences the concepts spoken about. It also has various uses; hence it is vital to develop a specific question or questions to help with seamless data analysis. It can, however, be time-consuming (Kawulich, 2004).
Thematic analysis
This method identifies the patterns of meaning in a set of data, such as the transcripts of a focus group or an interview set. The research is taken and grouped based on the similarities to help generate meaning from the presented data. Furthermore, it is crucial for exploring people’s views, opinions, and experiences. Therefore, the objectives and aims of the research involve having an understanding of the same (Sandelowski, 2000).
Grounded theory
This is a powerful method of qualitative analysis to create new theories using the data available via a series of revisions and tests. It requires the researcher to approach the study with open-mindedness. The research develops from the ground to the top. The analysis begins with the general population then moves to the small samples (Kawulich, 2004). A researcher develops a hypothesis from the general population, which is tested using a smaller sample. The theory has to create from the data and not a preconceived idea.
Interpretive phenomenological analysis
This technique has been specially designed to help with understanding a subject’s or respondent’s personal experiences. This method is centered on the subject, otherwise called the experiencer. Therefore, it is essential not to lose the depth of the meaning or understanding. The sample size is also smaller; hence broad conclusions become challenging to draw. Personal bias may be an issue with this method of analysis (Sandelowski, 2000).
Data analysis techniques in quantitative business research
Regression analysis
This is a widespread technique in quantitative research and is mainly employed by business organizations, economists, and statisticians. With this analysis method, statistical equations estimate or predict the effect that variables have on each other, such as how the interest rates influence consumers’ behavior (Cramer, 2003). It determines the correlation between one variable and another; positive or negative correlation.
Linear programming
Many companies often experience resource shortages such as labor, production machinery, and space. In such scenarios, the managers have the responsibility of identifying ways of appropriately and effectively allocating resources. This analysis method is designed to help with the determination of ways of achieving such solutions. It is also employed to determine optimal profits and reduce operating costs like labor (Cramer, 2003).
Data Mining Technique
This combines statistical methods with computer programming skills. This method continues to grow in popularity and parallel to the increased quantity and the volume of the data sets available. This technique is helpful in the evaluation of large data sets, mainly aiming to identify the correlations or patterns hidden within the data.
Issues of Validity
Overall, validity indicates the soundness of research. It applies to both the methods used in the study and the design of the study. Validity is a critical aspect of data collection. Valid data collection means that the research findings are an accurate representation of the phenomenon under investigation. In other words, they are solid. This is a crucial concern in research and can be affected by a variety of factors. Therefore, a good researcher should always ensure that any factor that might threaten or affect the validity of the research is controlled as early as possible. Various factors affecting validity include history, maturation, subject variability, the sensitivity of the task or instrument, the subject population size, and the time allocated for the collection of data and experimental treatment processes (Noble & Smith, 2015). There is external validity and internal validity. Internal validity is affected by mistakes that may occur within the study, such as failure to control some significant variables, a case of design problems, or an issue of data collection. External validity shows the degree to which the findings are generalized to a context or a larger group. If research does not have external validity, its conclusions can only be applied in the context in which it has been carried out and not any other context. Research findings become externally invalid if they cannot be used or extended to the contexts outside which the research was carried out (Noble & Smith, 2015).
Issues of Reliability
Reliability in research describes how consistent a measuring test or research study is. If the findings of a research study are consistently replicated, then the results are said to be reliable. However, if there is no consistency in the research findings, then the research is unreliable. To assess the degree or the extent of reliability, a correlation coefficient is used. A reliable test shows a highly positive correlation value. However, it is doubtful that in obtaining the correlation coefficient the identical results will be generated. This is because situations and participants vary. Despite this, a high positive coefficient value between the same research study or measurement test results shows reliability. Similar to validity, there are also two reliability types; external and internal reliability. Internal reliability evaluates the consistency of research findings or developments in the same test, while external reliability indicates the degree to which the items use vary (Golafshani, 2003).
Sampling Strategies and Techniques
In a research study, a sample is selected to be representative of the general population. There are various sampling techniques broadly categorized into non-probability and probability sampling techniques. In random or probability sampling, a researcher begins with the complete data set, and every participant has an equal prospect of being part of the sample data. In non-random or non-probability sampling, individuals don’t have an equal chance of being selected. As a result, the sampling error cannot be estimated, which may eventually lead to results that are non-generalizable (Landreneau & Creek, 2009).
Simple Random Sampling
The participants of a sample to become a representation of the general population are selected randomly. This technique of sampling is applied when the target population of the research is significant.
Stratified Random Sampling
There is a division of a large population size into small groups called strata in this sampling method. The members to form the representative sample are selected by chance from each stratum. The segmented strata overlap each other.
Cluster Sampling
This is a sampling technique that uses the significant segment divided into smaller groups called clusters. Commonly, the demographic and geographic parameters are used for this segregation.
Systematic Sampling
This is a strategy where a sample’s starting point is selected randomly while the other elements are chosen against fixed intervals. The interval is then calculated by performing simple mathematical operations, dividing the available size of the population by the size of the target sample (Landreneau & Creek, 2009).
Convenience Sampling
The items of a sample are selected primarily due to the closeness to the researcher. It is easy to implement the samples since selection is not made against any parameter.
Consecutive Sampling
This technique is similar to the conventional method. Still, the researcher can choose either a single item or a sample group and consecutively carry out research over a given period and repeat the same process on the other samples.
Quota Sampling
A researcher chooses the sample items based on their knowledge of the personalities and traits of the target sample to form a group. Members of the given groups are then selected to create the representative sample based on the researcher’s understanding.
Snowball Sampling
This technique is applied to the target audiences who are challenging to reach and get information. This technique is common where there is a rarity in putting together the target population or audience (Landreneau & Creek, 2009). With this technique, the participants are responsible for recruiting more participants for the study to be conducted. As stated previously, it is primarily used where potential audiences are difficult to find.
Judgemental Sampling
It is a sampling technique where the sample is created depending on the researcher’s skill and experience. It is also known as purposive sampling. The knowledge of the researcher is a crucial instrument when using this technique of sampling. The researcher is responsible for selecting every participant to become a member of the representative sample (Landreneau & Creek, 2009).
Question Two
The following is a data set with a rudimentary descriptive analysis done using Excel. The data has been obtained from the World Health Organisation information on COVID-19.


Mean
The mean of data is a significant concept in statistics. It is defined as the average or the most typical value in a set of data. It is used as a central tendency measure to determine a distribution’s probability, mode, and median. It is also called the expected value of a data distribution set. This concept can be calculated differently, but the most popular calculation methods are the arithmetic mean and the geometric mean.
The arithmetic mean is inferred by dividing the total value of the data presented by the number of items. The geometric mean is inferred by calculating the nth root of the product of the whole numbers within a collection and includes the compounding effects and volatility of returns. Therefore, the geometric average is more accurate (Palmeri & Nosofsky, 2001).
Mode
This is the value or data that mostly appears in a data set. The value appears repeatedly more than any other data within the given set. As a result, it has a high probability of being sampled. It is also a measurement of central tendency and is used to measure various important information regarding random populations or variables. Its numerical value is similar to that of the median and means within a data set of a normal distribution.
Median
In statistics, the median is the value that separates the lower half and the upper half within a set of data or a population. Simply put, the median is the value at the middle when the data is arranged in an ascending or descending order. This central value is not skewed by smaller portions or extremely small or substantial values. As a result, it gives better represents the “typical” value. It is also crucial in statistics because it’s the most resistant since it has a breakdown point of 0.5, provided that data contaminated is not more than half (Palmeri & Nosofsky, 2001).
Standard Deviation
This is the measure of the degree of data variation in a given set of data. When the standard deviation value obtained is low, most deals are closer to the mean of the data set, but when the value is high, it indicates values spreading widely. It is obtained by calculating the square root of the variance.
Sample Variance
This statistical concept is used to measure variability and derive the average of the squared mean deviations. It determines the extent to which data in distribution is spread. When the spread is large, the variance is also significant, and vice-versa.
Kurtosis and skewness
Skewness measures the symmetry or asymmetry of data when graphically represented, while kurtosis measures if the data is light or heavy-tailed relative to the normal distribution. A high kurtosis value represents a heavy tail end, also called outliers. Symmetric data have a close-to-zero skewness, while a negative skewness indicates skewed data to the left. Positive skewness indicates the data is skewed to correct, meaning the right tail is longer.
Range
This is the difference between the higher value data and the lower value obtained by subtracting the smaller value from the more significant value. It is essential as it gives an overview of how the outcome of a set of data will look like.
Correlation Coefficient
This is a mathematical concept that is used to measure the correlation between variables or data sets. It calculates the covariance, and the results always lie between +1 and -1. It only reflects the linear relationship between different variables, ignoring other types of correlation or relationships. A value of zero shows no relationship, while a positive value shows a positive relationship, and a negative value shows a negative or detrimental relationship between variables (Kaur, Stoltzfus & Yellapu, 2018).




References Antwi, S.K. and Hamza, K., 2015. Qualitative and quantitative research paradigms in business research: A philosophical reflection. European journal of business and management, 7(3), pp.217-225. Cramer, D., 2003. Advanced quantitative data analysis. McGraw-Hill Education (UK). Golafshani, N., 2003. Understanding reliability and validity in qualitative research. The qualitative report, 8(4), pp.597-607. Kaur, P., Stoltzfus, J. and Yellapu, V., 2018. Descriptive statistics. International Journal of Academic Medicine, 4(1), p.60. Kawulich, B.B., 2004. Data analysis techniques in qualitative research. Journal of research in education, 14(1), pp.96-113. Landreneau, K.J. and Creek, W., 2009. Sampling strategies. Available on: http://www. natco1.org. Lochrie, S., Curran, R. and O’Gorman, K., 2015. Qualitative data gathering techniques. Research Methods for Business and Management. Luna?Reyes, L.F. and Andersen, D.L., 2003. Collecting and analyzing qualitative data for system dynamics: methods and models. System Dynamics Review: The Journal of the System Dynamics Society, 19(4), pp.271-296. Noble, H. and Smith, J., 2015. Issues of validity and reliability in qualitative research. Evidence-based nursing, 18(2), pp.34-35. Palmeri, T.J. and Nosofsky, R.M., 2001. Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization. The Quarterly Journal of Experimental Psychology Section A, 54(1), pp.197-235. Sandelowski, M., 2000. Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed?method studies. Research in nursing & health, 23(3), pp.246-255. Silverman, D. ed., 2020. Qualitative research. Sage Publications Limited. Sukamolson, S., 2007. Fundamentals of quantitative research. Language Institute Chulalongkorn University, 1, pp.2-3. Taheri, B., Porter, C., Valantasis-Kanellos, N. and König, C., 2015. Quantitative data gathering techniques. Research methods for business and management: A guide to writing your dissertation, pp.155-174. Watson, R., 2015. Quantitative research. Nursing Standard (2014+), 29(31), p.44. Sheet1
COVID-19 status as of 2021
Country Cases Recovered Deaths
Kenya 160000 115000 3073
United States 33200000 31200000 591000
India 26900000 24100000 307000
Brazil 16100000 14200000 450000
France 5200000 361000 108000
Turkey 5200000 5050000 46621
Russia 4950000 4570000 117000
United Kingdom 4440000 3780000 128000
Italy 4190000 3790000 125000
Germany 3660000 3660000 87461
Spain 3650000 2970000 79801
Argentina 3560000 3130000 74480
Columbia 3250000 3030000 85207
Poland 2870000 2630000 72945

Mean 8380714.286 7327571.429 162542
Mode 5200000 ERROR:#N/A ERROR:#N/A
Median 4315000 3720000 97730.5
Standard deviation 9906562.288 9312846.394 168212.8776
variance 9.113E+13 8.05342E+13 26274459905
Kurtosis and skewness 2.629510208 2.883045444 2.58591053
Range 33040000 31085000 587927
Correlation coefficient 0.9915480909 0.9113998826 ERROR:#DIV/0!