Research Design, Workflow, and Methods - Literature review Example

What is Research? Research is “something that people undertake in order to find out things in a systematic way, thereby increasing their knowledge.”1An important component of this definition is ‘systematic’, meaning research is approached logically. Research describes, explains, understands, criticizes, and analyzes a subject area. Research is important in business and management. Research must produce practical conclusions leading to better decision-making and/or profit.2 Managers draw on knowledge developed by other disciplines to aid their work routine, and tend to prefer to conduct research with personal or commercial advantage. In research, combining two knowledge bases sometimes creates a single large knowledge base plus a new one. ‘Basic’ research generates new knowledge and options, whereas ‘applied’ research generates new knowledge with a practical aim.3 New insights can help change the way business-related activities are conducted. Both basic and applied research is used in healthcare. Health research may examine cost-effectiveness, efficiency, and appropriate and/or acceptable health services in primary and secondary health care.4 Health research does not overlap with audit and quality assurance, as the former assesses whether an aim has been achieved while the latter monitors it. Deductive and Inductive Research Both deductive and inductive research have strengths and weaknesses which make them appropriate for use in certain situations, and may be used in different phases of the same study, depending on its requirements. The deductive approach starts with a general hypothesis which is refined as information is gathered. Refining the hypothesis allows narrowing of the study’s focus onto areas of interest as defined by the data. Deductive research is often applied to questionnaires and the collection of quantitative data. Deductive research is a structured approach based on scientific principles. It is easy to standardise, so the researcher need not be highly knowledgeable about the topic. The time-consuming phases are set-up and data collection, so the amount of time needed for such research can be scheduled and predicted easily. Deductive research is relatively low risk, however there are some concerns. Causal relationships between all variables in the research must be defined. Sample sizes must be large enough to generate conclusions that are reliable in all applications of the data. Abstract concepts must be clearly defined to reduce ambiguity for respondents, and increase reliability of data. One of the main disadvantages of deductive research is that it relies on subjects to complete and return questionnaires, which may become problematic as sample size increases. The methodology of such research tends to be too inflexible to permit alternative explanations – if data does not support the hypothesis, then a new hypothesis must be formed, and new research carried out. The inductive approach requires the researcher to have a good understanding of the context of the research. Face-to-face interviews are essential to the research and gaining qualitative data is the primary focus. Inductive research usually has a flexible structure, allowing change in the focus of research. The researcher is part of the research process, enabling the process of gathering data to be dynamic and interactive. Because the focus is on qualitative rather than quantitative data, there is less concern with producing generalised conclusions. The research has a wider focus, allowing the researcher to see an overall picture of relationships between variables. The main disadvantage of inductive research is that studies tend to be lengthier, as data collection and analysis is carried out over an extended period. The big picture often emerges only gradually and it can be some time before useful data patterns emerge. Research Design Five research models will be examined: experimental design, cross-sectional surveys, longitudinal design, case studies, and comparative design. Experimental Design Experimental design is important because appropriately-designed experiments produce more reliable results. Independent variables should be manipulated to determine how they influence dependent variables. There are several forms of experimental research. Classic design is based on manipulation of independent variables, observations made before and after testing an experimental group, and relating observations to the dependent variable. The field experiment is used in experimental fields such as social psychology. The main advantage is that the researcher has no influence over experimental arrangements. Milgram’s (1974) electric shock experiment or Zimbardo’s prison experiments, for example, provide knowledge of how individuals and groups perform under certain conditions. Quasi-experiments are studies that have certain characteristics of experimental design but do not fulfil all internal requirements (e.g. control groups may be absent). Quasi-experimental design is useful in comparison and evaluation studies. Cross-sectional Design Also known as social survey design, this normally involves questionnaires and structured interviewing. Observation is a key feature of such research. Qualitative or quantitative data is collected at a single point in time, and is examined for patterns in relation to two or more variables. Longitudinal Design Longitudinal design, usually an extension of cross-sectional research, is used to plan changes in business and management. Longitudinal design provides more than one time-point for analysis, allowing more fundamental inferences to be made. There are two types of design. A panel study is often a randomly selected national study which collects data from various types of cases at two or more time points. A cohort study is a randomly-selected sample of people who share certain pre-determined characteristics such as birth-date or employment status. Both types are concerned with revealing social change and improving the understanding of causal influences. They also share similar problems. First, there is the problem of sample attrition through employee job changes, or companies going out of business. The remaining people may not form a representative group. Additionally, many longitudinal studies are poorly thought-out and may result in large amounts of data with little apparent use. Lastly, continued participation in such studies may influence how respondents behave. Case Studies Stake (1995) observes that case study research is concerned with the complexity and nature of the case in question. A case can be an organization, location, person, or event. Case studies may be linked with qualitative research using tools such as participant observation and unstructured interviews. There are three types of case study. The critical case is carried out with a clear hypothesis in mind, the unique case is used for clinical studies with a common focus, and the revelatory case is used “when an investigator has an opportunity to observe and analyse a phenomenon previously inaccessible to scientific investigation” (Yin 1984:44) Case studies can be conducted both deductively and inductively. Comparative Design Comparative design applies similar research methods to multiple contrasting cases, and can be applied in both qualitative and quantitative research to provide multiple levels of analysis. Such research takes the form of multiple-case studies with two or more organizations, teams, or individuals with certain common characteristics as cases for comparison. Research Strategies Research strategy is about developing an approach to answer specific research questions. Valid reasons must be clearly demonstrated for the research strategy that is chosen, for the questions that are asked, and for the subjects that are chosen for study. A good research strategy may use more than one type of research method. Surveys are usually associated with the deductive approach. Surveys are a popular and common strategy in business and management research, because they can be done independently, and allow large amounts of data to be collected economically. Survey data is normally obtained through use of a questionnaire, and is standardised and easy to compare and understand. However, surveys can be time-consuming to organise and analyse, and the number of questions must be limited so the goodwill of participants is not taken advantage of, which limits the scope of the survey. Action Research focuses on gathering information and enacting change within an organisation. As Coghlan and Brannick (2001) note: “the purpose of research and discourse is not just to describe, understand and explain the world but also to change it”. The researcher is involved in applying knowledge gained to implement change within the company. Employee involvement throughout the process is important, as they are more likely to apply changes they have helped create. Practitioner-Researcher: when the researcher also works within the company, they have a significant advantage in having knowledge of the organisation and how it functions. This is also a disadvantage as it can lead to assumptions and preconceptions when analysing data and forming theories. Care must also be taken to ensure that data-gathering is not inhibited when working with senior colleagues. Combining two different roles at work can also be very demanding. Using Multiple Methods Various research methods can be used in conjunction with each other, and it is often beneficial to do so. Each method is useful for carrying out different purposes within a larger study. For example, a general survey could gather initial data, followed by interviews to focus on key issues raised by survey results. The use of multiple methods also helps to ensure that the story the data is telling is accurate. Each method, has unique strengths and weakness (Smith 1975), and there is a relationship between the data collection method used and the results obtained. Using different methods cancels out the ‘method effect’, allowing more accurate conclusions to be reached. Questionnaires ‘Questionnaire’ is a general term describing data collection techniques in which subjects respond to a set of questions in a predetermined order. (deVaus, 2002) It includes structured interview and telephone questionnaires as well as written surveys. Questionnaires contain standardised questions that can be interpreted confidently in the same way by all respondents. (Robson, 2002) Questionnaires can be classified into two categories: structured questionnaires that involve fixed questions with pre-coded response choices (such as online, postal, delivery and collection, telephone survey), and semi-questionnaires that are defined by a more flexible interaction with respondents (such as an interview schedule). Both types must link back to research questions and objectives. Structured Questionnaires have an unambiguous approach which enables easy collection and analysis of quantitative data. This allows large numbers of respondents to be surveyed at once, making data collection economical and simple. However, the pre-coded responses in structured questionnaires means that some respondents may choose responses that do not entirely reflect their opinions. Structured questionnaires are intended to provide options for all respondents but this is not always possible, leading to errors in data collection. There is also scope for bias as respondents may sometimes desire to present a particular image in the answers they choose. Interviews allow for a better understanding of the context of the data and more flexible interaction with subjects. More complicated and deeper questions can be asked, and more information and in-depth understanding can be obtained. Inconsistencies and misinterpretations can be avoided, as clarification can be asked for by either party if needed. Response rate is generally higher than with other questionnaire methods (such as postal or telephone). However, such methods can be costly and time-consuming, and there is possible bias in conducting the data collection. A well-trained data analyst is essential to reduce potential bias. If both structured and semi-structured interviews are well-designed, highly accurate data can be obtained. Computer-assisted Questionnaires allow respondents to type in their answers on a computer screen. Inputting each answer prompts the display of the next question in the series. Statistical Analysis and Evaluation of Research Raw data must be collected and analyzed before it can be interpreted. Bowling defines evaluation as “the use of scientific method, and the rigorous and systematic collection of research data to assess the effectiveness of organizations, services and programs in achieving predefined objectives.”5 There are many ways of evaluating services and management techniques. Managers can improve their performance by learning evaluation skills and methods that are applicable to their daily routine. They are also encouraged to critically assess and making greater use of evaluations conducted by others. Evaluation can help allocate resources in a rational way, protect the business by remaining cost-effective rather than politics-driven, and make better forecasts of impacts of interventions. There are two basic evaluation types: formative and summative evaluation. Formative evaluation seeks to strengthen or improve the object of examination, while summative evaluation examines effects or outcomes related to it.6,7 Appendix I: Research Functions Basic Research Applied Research Generates new knowledge and options Generates new knowledge with a practical aim Understanding theory Developing tools and simulations Developing instrumentation and measurement techniques Tracking and absorbing external knowledge Appendix II: Types of Evaluation (Trochim 2004) Formative Evaluation needs assessment determines who needs the program, how great the need is, and what might work to meet the need evaluability assessment determines whether an evaluation is feasible and how stakeholders can help shape its usefulness structured conceptualization helps stakeholders define the program or technology, the target population, and the possible outcomes implementation evaluation monitors the fidelity of the program or technology delivery process evaluation investigates the process of delivering the program or technology, including alternative delivery procedures Summative Evaluation outcome evaluations investigate whether the program or technology caused demonstrable effects on specifically defined target outcomes impact evaluation is broader and assesses the overall effects – intended or unintended – of the program or technology as a whole cost-effectiveness and cost-benefit analysis address questions of efficiency by standardizing outcomes in terms of their dollar costs and values secondary analysis re-examines existing data to address new questions or use methods not previously employed meta-analysis integrates outcome estimates from multiple studies to arrive at an overall or summary judgment on an evaluation question Appendix III Coding Categorical Data 1. Existing coding schemes such as industrial classification, occupation, and socioeconomic group should be used as much as possible because they save time, are normally well tested, and allow comparison of results with other surveys. 2. Coding at data collection. 3. Coding after data collection is necessary when the likely responses are unclear. 4. Coding missing data may be necessary due to a number of factors. For example, respondents may not answer certain questions due to lack of knowledge or ambiguity in the question. Read More