Free Custom «Chapter 3: Research Methods» Essay Paper

3. Introduction

The research methods chapter is one of the crucial sections since it highlights the necessary information needed to evaluate the reliability and validity of the research. As a result, providing an accurate description of the research method and its individual rationale are equally vital in affirming the validity of this report. The research methodologies deployed are dictated by the research questions and the context of the study. Cheung & Hew (2009), cited that experimental study has the primary aim of elucidating the present state of affairs by using predetermined measurement variables. Additionally, it is vital to take into account the perception that the study relies more on probability. As such, the study methods used in this research attempts to explain the role accomplished by specific predetermined measurement variable in influencing the outcome. It is also equally imperative for this study to place much emphasis on the results, together with the comparison of the available conceptual approaches in order to show the impact of tax increment financing on development in Los Angeles city (Cheung & Hew, 2009). The research methodology chapter will discuss the methods used in this study. This methods will include a discussion of study design, methods of data collection, population and sample size, procedures used in sampling, research instruments and data analysis.

 

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3.1 Research Design

According to Dawson (2002), research design refers to a general plan that outlines the necessary steps needed in responding to the research questions and fulfilling the objectives of the study. Kothari (2008), added that design of the research has accurate objectives drawn from the research questions and highlights the sources of data to be collected. According to Kumar (2005), research design incorporates the structuring of the study in order to spell out the variables and determine how they associate to each other. The structure of the study is utilized in collecting data and facilitating the answering of the study questions. As such, the research design simply represents an outline, which serves as a crucial guide for the research when collecting the required data for the study.

McBurney & White (2009), cited that the research design could take three forms that include quantitative and qualitative designs, or a combination of both methods depending on the study contexts and the structure of study questions. According to McNeill (2005), qualitative research design uses the analysis and evaluation of qualitative data with the chief objective of answering the research questions and arriving at a conclusion. On the other hand, quantitative research design involves collecting and analyzing quantitative data and statistical variables in order to arrive at a conclusion. According to Cheung & Hew (2009), quantitative research design makes use of investigative tools like questionnaires for collecting quantitative data. It is clear that this study will need to collect both qualitative and quantitative data in order to assess the impact of tax increment financing on development in Los Angeles (Cheung & Hew, 2009). The data collection method will include only secondary data sources in order to guarantee wide-ranging analysis of data.

Cheung & Hew (2009), cited that a qualitative study involves an inductive procedure of organizing the collected data into categories or groups followed by an establishment of the association and patterns present between the classifications of data. According to Cheung & Hew (2009), this definition of qualitative study means that data and their pertinent meanings are drawn from the context of the study. Panneerselvam (2004), affirms that qualitative research is a system of inquiry with the chief aim of setting up narrative, broad and holistic description of the understanding of the research concerning the phenomenon in question, which is the impact of tax increment financing in the development of Los Angeles. Walliman (2006), found out that qualitative study design depends immensely on the basic presumptions and viewpoints that raise the probability of breaking down a many-sided phenomenon into a number of dependent and independent variables. A necessary aspect of qualitative research design is that the method needs to avoid definitions, limitations and delimitations imposed by the researcher. This significant strength of qualitative research design presents little opportunities for bias and that results are a precise portrayal of reality (Cheung & Hew, 2009). According to McNeill (2005), this takes place due to the minimal or rare opportunities for the manipulation of the collected data, which is very helpful in arriving at post hoc conclusions. In addition, Dawson (2002), adds that the collected data does not rely on the previous conclusion. The qualitative component of this research used the inductive mode of inquiry due to the absence of a firm overemphasis or hypothesis on the study questions. The study utilized a grounded study strategy with the chief objective of using iterative and natural data relationship and collection processes. In the grounded research strategy, the current conceptual approaches and frameworks discussed in the literature review are the likely outcomes of the study.

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Apart from the qualitative research design, the research used quantitative methodology to gather pragmatic evidence concerning the use of and effectiveness of tax increment financing in the city of Los Angeles (McBurney & White, 2009). It is evident that the context of the research is explorative, which implies that quantitative research design is vital in answering the study questions. The practical data collected will be critical in assessing the impacts of tax increment finance on development in the city of Los Angeles. Quantitative research design, unlike the qualitative design, used the deductive approach, because the research objective marks the beginning of the research and terminates the measurement of empirical data, analysis and evaluation of the data. The underlying principle for incorporating quantitative design in the research design is based on the offered practical evidence to evaluate the impacts of TIF on economic development. The deductive approach was utilized since the study commenced with outlining the theoretical concepts such as economic development incentives, and local governments, after which it deployed empirical evidence to fulfill the research objectives. The scheme for the study majorly involved evaluating the present theory and formulating research questions and objectives.           

3.2.3 Data Collection and Research Instruments

According to Cheung & Hew (2009), the collection of data is a critical requirement for any research, as it plays a pivotal role in dictating the success of the research by offering channels for deducing conclusions. This research depended notably on the statistical and descriptive data since it made use of the probabilistic approach in evaluating the impacts of tax increment finance on economic development in Los Angeles. McNeill (2005,) pointed out the two major forms of data that can be collected by a researcher. They include secondary and primary data (Cheung & Hew, 2009). According to Panneerselvam (2004), primary is the data collected by the researcher via direct contact with the respondents. On the other hand, Walliman (2006), defined secondary data as the data collected from other researchers who studied similar aspects of the research. The underlying research tool utilized by this research includes public records concerning TFI. According to Cheung & Hew (2009), public records are pieces of information that are not confidential. This implies that they are accessible. The data collected from these public records was analyzed to answer the study questions and arrive at valid conclusions. This research deployed primary techniques of collecting data. The secondary methods of data collection included the available public records concerning the impact of TFI on economic development.

Secondary Sources of Data

According to McBurney & White (2009), secondary data is the data gathered by another person other than the researcher.  The common sources of secondary data concerning public policy include organizational records, census and data gathered via qualitative research or qualitative methodologies. On the other hand, primary data is the data gathered by the researcher conducting the study (McBurney & White, 2009). The analysis of secondary data saves time that might have otherwise been spent on gathering data, and especially in the case of quantitative data. Secondary sources of data offer high-quality databases, which would be impracticable for any research to gather on his or her own. Additionally, analysts of economic and social changes regard secondary data as crucial. This is because it is not possible to perform a new survey, which adequately captures the past developments or changes (Cheung & Hew, 2009). This implies that without secondary data, this study might not be able to capture the changes in development as influenced by tax increment financing.

Cheung & Hew (2009) referred to secondary data as information collected for the purposes other than the completion of a study project. Various sources of secondary data are available to the investigator collecting the data on the impact of TFI on economic development. In this context, secondary data will be used to gain initial insight into the research questions. Secondary data are categorized based on their sources (Cheung & Hew, 2009). As such, two significant categories include external, and internal or in-house sources of secondary data. Internal or in-house secondary data refers to the data acquired within an organization, institutions or a company within which research is conducted (Cheung & Hew, 2009). External secondary data are acquired from outside sources. In this context, internal secondary data will be obtained from local government institutions within Los Angeles city. On the other hand, external secondary data will be obtained from other external sources outside Los Angeles city.

The two most recognized benefits of secondary data are cost and time savings. The process of conducting secondary research can be accomplished rapidly (Cheung & Hew, 2009). A skillful analyst can gather significant secondary data in few days. The availability of public records that will be used as the major source of data will enable the researcher to not only locate the source of data but also extract the needed data (Cheung & Hew, 2009). According to McBurney & White (2009), secondary data is usually less expensive than primary study. The gathering of information concerning the effects of TFI on economic development does not need the deployment of specialized and highly trained personnel.

Internal secondary data are an inexpensive source of information for the research. In fact, it is the place to begin the present operations (Cheung & Hew, 2009). The use of internal secondary data is to describe the competitive position of the city of Los Angeles. The various sources of internal secondary data include public records containing accounting and financial records, and miscellaneous reports. These are frequently ignored source of internal secondary information. Accounting records will be deployed in evaluating the success of various TFI projects in the city of Los Angeles (Cheung & Hew, 2009). Nevertheless, there are limitations related to the use of public records. The first limitation is the factor of timeliness (McBurney & White, 2009). It takes several months before public records are made available. Another limitation concerns the structure of these records. Usually, most institutions do not sufficiently set up their records to offer the answers to the research questions they need. Public records can be obtained from various sources. They include federal government, provincial or state governments, trade associations, statistics, general business publications, annual reports, library sources, and computerized bibliographies (McBurney & White, 2009).

3.3 Data Analysis

Kothari (2008), points out that data analysis involves three primary processes: examination, conversion and modeling of the collected data. Kumar (2005), adds that the chief purpose of the data analysis is to highlight helpful data to assist in arriving at conclusion and back the process of making decisions (Cheung & Hew, 2009). In relation to the context of this research, the role of data analysis was to make conclusions and answer the research questions. This study deployed both inferential and descriptive statistics to arrive at conclusions from the collected data. Descriptive statistics was vital in describing and summarizing data by statistical variables like mean, mode, proportions and percentages in order to analyze the trends and patterns from the collected data (Cheung & Hew, 2009). The limitations linked to descriptive statistics cannot be used in inferring conclusions. Instead, they are used in describing data. According to Panneerselvam (2004), inferential statistics are vital in the generalizations of the collected data during the research. Dawson (2002), pointed out that inferential statistics utilize statistical variable such as standard deviation in arriving at generalizations concerning the impacts of TIF on economic development in Los Angeles. The method used to analyze the data involved bivariate and univariate data analysis (Cheung & Hew, 2009). Univariate data analysis involves examining the distribution of a single statistical variable at a time. On the other hand, bivariate analysis involves the deployment of contingency tables for relative analysis (Dawson, 2002).

According to McBurney & White (2009), the reuse of qualitative data offers an exceptional opportunity to investigate the raw materials of the attest or distant past in order to gain insights for both theoretical and methodological purposes. In the qualitative analysis of secondary data, good documentation cannot be overlooked (McBurney & White, 2009). According to Cheung & Hew (2009), this is because it offers the required background and the required context that make reuse more systematic endeavor and worthwhile.

3.4 Reliability and Validity of the Data

Cheung & Hew (2009), mentioned that reliability has the key objective of guaranteeing consistent research results. On the other hand, validity has the key objective of ensuring integrity of the arrived conclusions. In order to eliminate any possible bias in the information collected concerning the impact of TIF on development in Los Angeles, the research ensured that the public records used from the reliable government institutions. Additionally, the research opted for these institutions because they are more informed about the tax increment financing (Cheung & Hew, 2009). The reliability and validity of the study methods were asserted using a pilot study with peer to identify any ambiguity and difficulties of questions in the questionnaires.

3.5 Limitations of this Research

According to Cheung & Hew (2009), secondary data are easy to find and gather. Nevertheless, the research needs to be aware of the disadvantages the data might have on study. Problems might arise if these limitations of secondary sources are not considered. The first disadvantage associated with secondary data is that they can be general and vague. Public records, like any other secondary data, can be vague and might really assist in the making of the right conclusions (Cheung & Hew, 2009). Public records might also be accurate. As such, the researcher must always check the source of the data contained in these public records. According to Cheung & Hew (2009), public records might also be old and out of date. As such, they might cause the research to make wrong conclusions. The sample deployed in generating some of the data in public records might be extremely small. Conclusions that are based on such small samples might not reflect the actual situation on the ground. Additionally, according to McBurney & White (2009), the institution publishing some of the public records might not be reputable.

3.6 Conclusion

This chapter has discussed the study methods and the individual justifications that are necessary in guaranteeing the reliability and validity of the research. The research methodologies deployed are dictated by the research questions and the context of the study. The data collection method will include only secondary data sources in order to guarantee wide-ranging analysis of data. Secondary data is the data gathered by another person other than the researcher.  The common sources of secondary data concerning public policy include organizational records, census and data gathered via qualitative research or qualitative methodologies. Research design could take three forms that include quantitative and qualitative designs, or a combination of both methods depending on the study contexts and the structure of study questions. Qualitative study involves an inductive procedure of organizing the collected data into categories or groups followed by an establishment of the association and patterns present between the classifications of data. The first disadvantage associated with secondary data is that they can be general and vague. Public records, like any other secondary data, can be vague and might really assist in the making of the right conclusions In relation to this research context, the role of data analysis was to make conclusions and answer the research questions.