Cross-Sectional Studies and Analysis - Essay Example

Title: Cross-sectional Research Method Course Professor’s name Institution Date Cross-sectional Research Method The cross-sectional research design is one of the most popular research design compared to the rest. This design is also referred to as Social Survey design. According to (Bryman and Bell, 2007), cross-sectional research design involves the collection of data on more than one case and at a single point in time to collect a body of quantifiable or quantitative data connected with two or more variables, which are then examined to detect patterns of association. Cross-sectional research design has three different features: no time dimension, dependence on existing differences instead of change following intervention, and finally, selected groups are picked by their existing differences rather than random allocations. Cross-sectional research design usually measures differences found between and from different people, phenomenon or subjects instead of change. Therefore, researchers using this method can only use a relatively passive approach when making causal inferences based on findings. Cross-sectional analysis is used to identify special characters available in a group of comparable organizations instead of establishing relationships. This kind of analysis is based on information and data collected and seek to understand the "what: rather than the "why." The cross-sectional analysis will allow a person or business to build assumptions, and later test hypothesis based on the research methods. Cross-sectional research studies data collected at a single point in time as opposed to over a period. Studies begin with the establishment of research goals followed by definitions of variables the studies want to measure. After this, the study will identify the cross-section, like a group of peers or industries, and specify the exact time of the assessment. Finally, cross-sectional studies will conduct the analysis based on the cross-section, variables and form a conclusion on the performance of the business or organization. Examples of Cross-sectional Studies While cross-sectional studies are used in analyzing a business or an organization, it can be used further to analyze various scopes in an organization. For instance, a study by Tinbergen Institute Amsterdam in 2016 measured the factor timing ability displayed by hedge fund managers. Factor timing shows a managers ability to time markets correctly when investing and how they take advantage of different market movements such as expansions and recession. Cross sectional research was used to reveal that managers using leverage to their advantage and those who were smaller, newer, and more agile with high incentive fees and less restriction period were much better. This analysis was able to help investors select their best hedge funds as well as their managers (Osinga et al., 2016). Benefits of Cross-sectional analysis Most often than not, decision makers will make decisions based on hunches. Even if the guess is an educated one based on observed trends, making the right choice will rely on acting on the actual data from the actual audience. For business researchers looking to give insight on a smaller scope, cross-sectional studies are more effective way of collecting data they need and more realistic to form decisions. Benefits of cross-sectional analysis vary by project; therefore, one should choose one of these three factors while choosing a cross sectional study. These benefits of cross-sectional analysis are: Faster – Since a cross-sectional study is a one-time operation, analysts will be able to analyze and act upon the data immediately. Cheaper – since this analysis only happens once, expenses are reduced as compared to performing analysis over time and multiple studies found in the longitudinal analysis. Easy to manage – building by being a one-time affair, the cross-sectional analysis is much easier to manage and analyze the data effectively. Longitudinal Research This type of research design occurs on many touch points across an extended period. In most cases, it is observational, that is, the surveyor is not interfering with the survey respondents or the subject. Subjects are surveyed numerous times and in some cases over several years. While longitudinal research has frequently been used in the medical and scientific field, they have beneficial to businesses. There exist three kinds of longitudinal studies mainly panel, retrospective, and Cohort. A panel study involves a representative sample of subjects that are often found via a panel service company. As for the retroactive study, longitudinal research takes advantage of historical data that is compared to more updated data. Finally, cohort study observes subjects that share a group or a demographic based on their characteristics. This could include age, region or common experience. Conducting longitudinal analysis is demanding, meaning that it requires appropriate infrastructure that is sufficiently strong to withstand the test of time for the extended period. It is paramount that the data collection methods and recording are similar across all study fields as well as being standardized and consistent over time. Data collected needs to be classified according to the measured interval, with all the data relating to a particular individual is linked using unique coding systems. Adopting recognized classification systems will enable facilitation of records and increased accuracy in data collected. While conducting longitudinal analysis, many variables are considered and effectively controlled such as factors about the population in question and their environment. Also, stability regarding distribution and geographical mobility together with the ability for continued follow up should there be displacement is key to a successful study. After laying out an adequate design, the implementation of longitudinal analysis may need a significant amount of time; especially if it’s being conducted over multiple location sites. A lot of time should be invested in this operation to ensure data accuracy from data collected. There is a need for regular monitoring of outcome measures as well as focused review on areas of concern. Finally, since these studies are dynamic, it is important to update procedures regularly and the retraining of contributors. Examples of longitudinal research done over time include the USA 1968: Panel Study of Income Dynamics where some individuals were observed for more than 40 years, Germany 1984: Socioeconomic Panel Study with annual surveys for 25 years and the UK 1991: British Household Panel Survey with annual surveys for 18 years. The longitudinal analysis provides several benefits including: The ability to identify and compare events to particular exposures and to define further these exposures about timing, presence, and chronicity. It establishes a sequence of events Follows change over time in behaviors within the cohort Help account for each variable However, its disadvantages are: Takes too much time and resources Participants may have passed away Interviewers may refuse to participate Contact with participant may be lost over time Deductive Analysis Deductive analysis approach involves the development of a hypothesis based on existing theory and later designing a research strategy that tests the hypothesis. It is also referred to as an analytical, abstract or prior method. In other words, the deductive approach involves deriving conclusions from the general truths and applying them to form conclusions. It has been mentioned that "deductively refers to the reasoning from a particular to the general. If an underlying relationship or link seems to be inferred by a certain theory, it may be true in most cases. Deductive analyses may test to see whether the relationship or link was obtained under more general circumstances", (Gulati, 2009). Deductive approach is explained via a hypothesis that is derived from the propositions of a theory. Therefore, a deductive approach is used to deduct conclusions from propositions or premises. Deductions start with an expected pattern that has already been tested against observations. In deductive reasoning, when the data is true, then the conclusion is out rightly correct. For instance, if a product is in demand during the holidays for a while based on previous statistics, then it is true that the product will have higher sales during that period. There are five stages used while approaching deductive approach: Deducing a proper hypothesis from a theory Formulation of a hypothesis in operational terms and coming up with relationships between two specific variables. Hypothesis testing while applying the relevant methods. Examining the results of the test, and therefore confirming or rejecting the theory The final step involves modification of the theory in instances where the hypothesis is not confirmed. Benefits of Deductive analyses The main advantages that deductive analysis bring are: The method is near to reality, that is, it is less time consuming, and it's also less expensive Using mathematical techniques when deducing theories of economics allows analysts to get accurate and clear economic analysis Since the method involves a limited scope of experimentation, it assists in deriving economic theories The method is simple since it is analytical. Using Deductive analyses has its demerits: Since it’s based on assumptions, it will usually turn out as half-truths or don’t have any relation to reality thus may be misleading Deductive method is hugely abstract, that is, it requires great care to avoid bad logic or false economic reasoning. Inductive Analysis The inductive analysis is also known as inductive reasoning involves observation and formulated theories are formed at the end of the research process. Inductive research, according to (Gulati, 2009) involves the search of patterns via a series of hypotheses. No hypotheses or theories would apply in inductive studies at the start of the research while the researcher is free regarding changing the direction of the study after the end of the research processes. It is key to emphasize that inductive analysis does not necessarily mean disregarding theories during the formulation of research objectives and questions. This approach helps generate meaning from the data collected to identify patterns and the resultant relationships to formulate a theory. Nonetheless, inductive analyses do not limit a researcher from using existing theories to create the research questions to be explored. Reasoning through in inductive research is mainly based on experiences. Resemblances, patterns, and regularities in experience are observed to reach a conclusion or generate a theory. By nature, an inductive analysis is more open-minded and exploratory, more so during the initial stages. The inductive analysis starts with detailed observations of the current world trends that move towards more abstract ideas and generalization. When following an inductive analysis, starting with a topic, an analyst tends to develop empirical generalizations and detect preliminary relationships as the research continues. There is no hypothesis that can be found during the former stages of the research while the analyst is not sure about nature and type of the research findings until the close of the study. In an Economic analysis, induction method is also referred to as empirical method and was adopted by Historical School of Economists. It involves reasoning from certain facts to the general principle. This method involves deriving economic generalizations by Observations, Statistical methods, and experimentations. Data is collected from a certain economic phenomenon and systematically arranged and an eventual conclusion drawn from them. Inductive Marketing techniques can be used in marketing. This requires accumulation of specific data to formulate general conclusions about consumers or products. Advertising marketers and professionals accumulate this data in various ways including customer satisfaction forms, consumer market surveys and sales figures from a specified market area. Accumulating this data takes a period and has a limited shelf life in regards to its effective use. It is important to do fast analyses for the advertising and marketing professionals to formulate conclusions to form strategies to attract consumer attention. Strategies formed from inductive marketing reveal the strongest success on the internet, whereby consumers can plug information into business websites and later a variety of options based on specific answers. There are several examples of Inductive analysis in the business; a market researcher may form a focus group to monitor consumer responses to a new product. Similarly, a stock broker may observe stock increase over a period and may recommend one to buy shares during a specific period. Benefits Businesses use inductive marketing with an extensive selection to increase chances consumers will buy their products. Based on these selections on the specific consumer needs and desires, it raises the chances for huge sales numbers. The problem with inductive analyses is the lack of a market research to predict sales figures and demand trends. Businesses using inductive analyses in marketing are at the mercy of consumer opinions and may lose some ability to some specific changes in the market since there is a need for continued offerings for generalized product/services. References: Bryman, A., Bell, E., 2007. Business Research Methods. Oxford University Press. Gulati, P.M., 2009. Research Management: Fundamentals and Applied Research, First edition. ed. Global India Publications Pvt Ltd. Osinga, B., Schauten, M., Zwinkels, R.C., 2016. Timing is Money: The Factor Timing Ability of Hedge Fund Managers.  Read More