Introduction
This paper will start by looking at data analysis strategies and techniques of analyzing qualitative and quantitative data. Qualitative and qualitative data must be clearly linked to each other in order to allow easier interpretation of data. This article has information about data analysis and interpretation in business research presented in a coherent and logical manner. Statistical and non-statistical methods found in modern business research in the analysis and interpretation of data have been presented. Business research is essential for both managers and decision-makers. Thus, business research should be done in the most professional way. There are various tools used to analyze and interpret business data. The focus of this paper is modern methods and analytical techniques available to decision-makers operating in a supranational business environment. The main objective is to highlight data analysis and interpretation techniques in a more comprehensive manner, with an emphasis on a collaborative problem-solving approach through exploration of actual business problems and data sets (Schoenbach, 2004).
Purpose of the Study
This study aims at identifying and selecting appropriate statistical and non-statistical tools for practical data analysis and interpretation. In business analysis, data analysis and interpretation should be applied to business decision-making. It should make sense when presented to a professional audience.
Background
The essence of data analysis and interpretation is to give meaning to what would otherwise be a mere collection of numbers and or values. However, the significance of data analysis and interpretation to business research depends on the clarity of the research question(s) or problem(s). This implies that one needs to edit data properly even before analyzing it in order to detect errors as early as possible. This can be done in a number of ways which include conducting consistency checks and range checks. This helps in highlighting what such data can or cannot accomplish in the research. During the processes of data analysis and interpretation, the researcher needs to be well versed with basic techniques such as data coding. In addition, the researcher may need to familiarize him or herself with the meaning of various basic business statistics terms used in the characterization of mathematical attributes of the different types of variables. This includes categorical, ordinal scales, nominal scales, interval scales, ratio, and count, discrete and continuous, among other types of measurement variables. Most business research will require that the researcher have a clear understanding of the mentioned type of variables. In addition, the researcher may also find it necessary to have an understanding of the meaning of a “derived” variable and the various types of derived variables.
Furthermore, the researcher must be able to recognize the advantages and disadvantages of the different kinds of variables and how to treat them in different ways.
Other important, relevant issues include the objectives of statistical hypothesis tests (also known as “significance” tests), the importance of the outcomes from these tests, and how to interpret them. In general, business research has taken a more dogmatic approach to statistics, but this should not be so since statistics is an integral part of research in general (Creswell, 2003).
Literature Review
Data analysis and interpretation always come after data collection, which is accommodated in the research design. Research design, in most cases, takes the form of qualitative, quantitative, or even a mixed approach. This implies that data analysis can be qualitative, quantitative, or both (Creswell, 2003).
Qualitative versus quantitative data analysis
In business research, qualitative methods provide answers to a number of questions. Ritchie and Spencer (1994) summarized these questions into four categories: evaluative, strategic, diagnostic, and contextual. In this case, it is clear that qualitative analysis questions are answered using qualitative and quantitative data. Proponents of qualitative data argue that it is best used for an in-depth comprehension of a given problem. This type of analysis answers questions like what, why, and how (Silverman, 2000). Quantitative analysis, on the other hand, utilizes statistical techniques in data analysis. There are majorly two types of statistics; descriptive statistics-used in the analysis of non-randomly sampled data and inferential statistics used in the analysis of randomly sampled data. The methodology gives answers on percentages of distribution, rating, variability of data, the relationship between two or more variables, and statistical significance of the results, among others.
Despite the empirical advantages of quantitative data analysis, it can be noted that the methodology does not adequately provide answers to questions on what, why, and how a phenomenon occurs (Denzin & Lincoln, 2000; Silverman, 2000). In order to clearly understand the processes or the what, how, and why of a given occurrence, qualitative research methodology offers the necessary comprehensive and exploratory techniques to achieve a clear view of the process. Collis, Hussey, and Hussey (2003) posited that qualitative research, when used in the business environment, offers a stronger premise for analysis and interpretation since it is derived from the natural environment of the occurrence. There are various tools used to analyze and interpret business data. However, it is imperative to note that the type of tool utilized by any research is dependent upon the main research question or problem (Denzin & Lincoln, 2000).
Qualitative data analysis and interpretation
Creswell (1998) defined qualitative research as,
“An inquiry process of understanding based on distinct and methodological traditions of inquiry that explore a social or a human problem. The researcher builds a complex, holistic picture, analyzes words, reports detailed views of informants, and conducts the study in a natural setting” (p. 15).
In qualitative research methodology, the researcher has to gather necessary information through observation, questionnaires, and surveys, among other methods of qualitative data collection. During this process, data must be edited before presentation as information, and this is done to guarantee accuracy. Editing of data can be done manually or electronically. Another factor that needs to be kept in mind when carrying out qualitative data analysis involves the handling of blank responses for questionnaires. If more than 25 percent of the questionnaire is blank, it is discarded. Coding is also an important part of qualitative data analysis. This is the final phase of quantifying qualitative data. Answers are coded in a manner that they can be understood by the computer. In coding, data sets are systematically condensed into smaller sets. Categorization is the process that follows after coding. In order to categorize data, it is imperative to divide it into classes or segments that are mutually exclusive, for example, age, religion, gender, etc. Nominal scales are utilized in this process, and all categories are based on the research query. Whether one is talking about per capita income, spending habits, or risk, categories must be well defined, and the items are listed in a reference table (Schoenbach, 2004).
After categorization, entering data is now what the researcher needs to focus on. In contemporary times, technological advancement has made the recording of data easier. For example, data can be collected on an answer sheet which can be scanned into a computer. This will enable the researcher to save such data directly into the computer. Alternatively, raw data can be manually fed into the computer as a file. In such cases, software such as SPSS data editor is very useful for entering, editing, and viewing. This has made it even easier to add, alter or even delete some values after the data has already been entered into the computer (Schoenbach, 2004).
Objectives of data analysis and interpretation
The final phase of qualitative research is the real data analysis and interpretation. Researchers have identified three major objectives of data analysis which are getting a feel of the data, accessing the validity and reliability of the data, and testing the hypotheses and to start, having a feel of data occurs when statements are well summarized, and tools of descriptive statistics are used for breaking down large data sets into smaller meaningful indicators presenting central tendencies and dispersions. There are three measures of central tendency that are used in statistical analysis, which are the mean, the median, and the mode, and each of the measures is designed to correspond to a particular score which means that the choice of the measure is dependent on the type of the distribution (normal or skewed) and also on the type of measurement scale, whether nominal, ordinal or interval.
The second purpose of analyzing data is to check for reliability and validity. In order for the data to be of use to the management, it should be both valid and reliable. Reliability is the measure of the dependability or the steadiness of the data. Validity represents the authenticity or genuineness of the data. The use of multi-methods helps in providing in-depth data and also in validating the findings, thus, in turn, increasing the research’s reliability (Yin, 2003).
Finally, the purpose of data analysis is to conduct hypotheses testing. Once data has been cleared-has passed the test of reliability and validity, the next task is to test the hypotheses formulated for the report. A hypothesis can be null or alternative.
Quantitative data analysis and interpretation
Quantitative data analysis utilizes statistical tools such as probability distributions, Measures of central tendency-measures similarity of data, and Measures of dispersion– measures dissimilarity in the data. In addition to the above are the measures of central tendency-these include: mode-observation with the highest frequency, median- simply the mid-point, and the mean, which is the expected value of the data. The statistical technique to be adopted is dependent on the type of data set a researcher has. The main types of measurement scales are nominal, ordinal, interval, and ratio scales. Data is analyzed using various measures such as; Measures of Dispersion-these include the range (the difference between the highest and lowest data.
Values), and the Standard deviation -measures the variability from the mean. Standard deviation is superior to the range because it enables each particular case to affect its value. The range is advantageous due to its simplistic nature of calculation (Schoenbach, 2004). Other techniques include correlation and regression analysis which looks at the relationship between variables. These techniques answer the question of how strong is the relationship between variables.
As mentioned above, the type of variables to be used in research should be well defined. Otherwise, the research would simply lose its meaning. There are two main types of variables; independent and dependent variables. Independent variables explain changes in the dependent variable, especially when valuing returns of a stock where political risk is an independent variable since it affects the returns of a given stock. Dependent variables are also known as explanatory variables, and they behave differently because, in the example above, returns of stock represent the dependent variables.
Findings and Analysis
Statistical Significance
Statistical significance tests are tools used to estimate the likelihood of the results being wrong and how likely that the results of the analysis are statistically significant (they are not obtained by chance) within a certain margin, say 95 percent. A test for significance is an estimation of the probability of getting the results by chance if there were no differences in the population. Most of the data analyzed does not go beyond 0.5 (Henk, 2004).
Important statistical tests
The most commonly used statistical tests include Chi-Square and t-Test. These tests are popularly used because they are easy to calculate and interpret. They are used to compare nominal data sets (such as marital status and age). These tests are also used to compare ordinal variables or even a combination of both nominal and ordinal variables. In addition, they are used to investigate whether one group of numerical results is statistically different from another group of results (Albright, Winston & Zappe, 2006).
Hypothesis Testing
In business research, the hypothesis is defined as the best guess of the relationship between variables. For example, ‘there is a difference between GDPs of less developed countries and those of industrialized nations.’ Normally, the researcher can state null and positive hypotheses (the above statement is a positive hypothesis) as the study demands. A null hypothesis is always a statement that negates the positive hypotheses. For example, ‘there is no difference in the GDPs of less developed countries and their developed counterparts.’ (Albright, Winston & Zappe, 2006).
Linking Qualitative and Quantitative Data
In business research, one of the questions researchers face is, should quantitative and qualitative data analysis methods be linked when designing a study? The real questions here are how and why? In practice, there are significant linkages between qualitative and quantitative data analysis methods. These linkages are done using techniques such as triangulation (corroboration). Triangulation is the use of more than one source of information to make date data more plausible. This is done by using three or more methods of data collection such as interviews, questionnaires, observations, historical data, and expert panels, among others. The importance of linking qualitative and quantitative data analysis methods is to create richer details, initiate new schools of thought, and expand the scope of the research. Defining the problem is critical to conducting successful business research, data analysis, and interpretation, and this may call for extra time because defining the research problem is important in successful data analysis and interpretation (Albright, Winston & Zappe, 2006).
Role of statistical computing in data analysis
The rapid and continued increases in computing technology dating back from the early 20th century have had a significant impact on the practice of business research, especially data analysis and interpretation. Early statistical analysis models were just linear models. However, powerful computers which have data analysis software such as excel, STATA, E-views, etc., have stimulated analysts’ interests in nonlinear models such as Logit models as well as the development of new models, such as multilevel models and generalized linear models (GLMs). In addition, the increased use of computers has also led to the creation of powerful computational methods which are based on re-sampling techniques, such as the bootstrap, Monte Carlo simulation, and permutation tests. Other models such as the Bayesian model have been made much more feasible due to the availability of techniques such as Gibbs sampling. It is apparent that the digital revolution will have more influence in the future, especially in the field of business data analysis and interpretation, and emphasis will be laid on empirical and experimental data analysis, which researchers do not focus on in the contemporary practice (Chance & Rossman, 2005).
Conclusion
In conclusion, data analysis in business research involves various steps. First, the necessary data is collected through various data collection methods such as questionnaires, surveys, and experiments. This data is then edited, coded, categorized, and entered in tables or computers to ensure that only accurate data is used in business research. After this, based on the research problem, hypotheses are formulated and tested using the most suitable and reliable measures. The results are then interpreted, and an appropriate solution is made to answer the research problem. Researchers will often find data analysis as the most enjoyable part of carrying out business research since, after all the toiling and patience, they now have a chance to find out the solutions to the research problem. In fact, if the analyzed data does not answer the research question(s) and this can be regarded as yet another opportunity to be creative. Thus, the analysis and interpretation of business data followed by the presentation of results are indeed the results of coding and recording of data. However, data does not have a voice of its own.
Data analysis is simply used to reveal what the analyst can detect, and this is the whole essence of research. Hence, when a researcher attempting to harvest these fruits of research finds him or herself alone with no clue on how to analyze and interpret a data set, there can be a feeling of anxiety rather than eager anticipation. After categorization, entering data is now what the researcher needs to focus on. In contemporary times, technological advancement has made the recording of data easier. For example, data can be collected on an answer sheet which can be scanned into a computer. This will enable the researcher to save such data directly into the computer. Alternatively, raw data can be manually fed into the computer as a file. In general, analysis and interpretation of business data should clearly relate to the research objectives and questions. Most analysts’ approaches would be to begin with descriptive analyses. This helps in exploring and gaining a “feel” for the data set. This helps the researcher to address specific research questions from the report or hypotheses, from research findings and study questions as reported in the literature review, and from trends and patterns presented in the descriptive analyses. This will ensure that data analysis and interpretation in business research are relevant and progressive.
References
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