Replication-based dissertations have the benefit of being highly extendable. In other words, you can choose to simply duplicate a study, or you have the option to make generalisations and other extensions in a wide variety of ways. This makes them very popular amongst undergraduate and master's level students. However, they also have their challenges, like any other type of dissertation. In the sections that follow, we discuss some of the benefits and challenges of taking on Route #1: Replication. When you read through the benefits and challenges, think of your own situation, interests, and skill sets, and ask yourself: Is Route #1: Replication right for me?
Whilst there are a wide range of benefits to taking on a replication-based dissertation, our top three reflect: (a) the overall difficulty and value of the contribution; (b) choosing your topic and setting up your dissertation; and (c) executing the dissertation. Each of these benefits is discussed in turn:
Unless you are a numbers nut who likes to delve into the data (i.e., Route #2: Data-driven dissertations), or you are particularly good at dealing with abstract concepts (i.e., Route #3: Theory-driven dissertations), it is likely that you will find Route #1: Replication-based dissertations the easiest. Since the dissertation process is already challenging, most students will opt for some kind of replication-based dissertation, even if in the majority of cases, they do not realise this is what they are doing.
With the exception of Route C: Extension, and in some cases, Route B: Generalisation, the contribution that replication-based dissertations (i.e., Route #1) make is not as great as Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations. However, this does not mean that a replication-based dissertation is of little value, or cannot achieve a high grade at the undergraduate and master's level [NOTE: we will be adding sections guiding you through Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations in early 2013].
You should note that high grades are not only based on the originality of your dissertation, but also its execution. Whilst the originality of replication-based dissertations is often less than data-driven and theory-driven dissertations, the ability to execute (i.e., carry out) these types of dissertation well is often easier. As a result, the contribution that you can make in some dissertations based on Route B: Generalisation and Route C: Extension can still be high when well-executed.
Route A: Duplication contributes to the literature by checking the internal validity and reliability of a study, as well as adding to the construct validity of the constructs that are studied. This is important because studies in the literature are seldom replicated (N.B., we explain more about the ideas of internal validity, reliability and construct validity later on). Whilst it is difficult to publish duplication-based studies, you may find, even though you are doing an undergraduate or master's level dissertation, that you can submit your findings for publication to a journal in what are known as Notes or Letters, which may look good on the CV! Route B: Generalisation can make a useful contribution to the literature by examining the external validity of the original study (i.e., the generalizability of the original study in terms of other populations, treatments, settings/contexts and time). Examining the generalizability of research is an important objective of quantitative research, and can make a useful contribution to the literature. We explain more about the specific benefits of the different types of generalisation you can follow deeper in Lærd Dissertation site. Route C: Extension has the benefit of taking the original study further. Some aspects of Route C: Extension are similar to Route B: Generalisation because the desire to make certain types of generalisation requires greater changes to be made to the original study during replication. However, extension-based dissertations often make more significant contributions to the literature because they go the furthest in showing how an original study can be developed.
One of the major benefits of replication-based dissertations is that the process of (a) choosing a topic and (b) establishing what you research questions and/or hypotheses are is relatively straightforward. Most universities have extensive access to journals across a wide range of subjects, which gives you so many options when it comes to selecting a study that you want to replicate. Even if you find that the study you are interested in is too hard to replicated, whether because of the research design and data analysis techniques used, or simply because the authors have not provided detailed enough guidelines to follow, it will not take long to find another suitable study.
In addition, it can be challenging to create research questions and/or hypotheses from scratch. These need to be clearly written, precise, and closely tied to your research goals. Whilst you may need to modify some of these research questions and/or hypotheses when following Route B: Generalisation, and even create one or two new ones for Route C: Extension, you are still able to build on those research questions and/or hypotheses set out in the original study.
This a huge benefit compared with as Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations, whose focus on originality means that (a) it is more challenging to come up with an original topic, and (b) research questions and/or hypotheses have to be created from scratch. Even though we provide extensive guides on how to do this, it can still be challenging.
Replication-based dissertations make it easier to execute your dissertation effectively. It is easier to effectively execute a dissertation when (a) the research design is clearly set out, (b) a reliable measurement procedure is available, and (c) the data analysis techniques used are explained. This is because you have (a) more guidance in how to carry out your research (i.e., the research design), (b) the ability to start collecting data sooner and with more confidence (i.e., a reliable measurement procedure), and (c) the chance to learn about the statistical techniques you might need to use to analyse your data long before you actually have to use them (i.e., data analysis techniques). Whilst it is likely that you will make some changes to the research design, measurement procedure, and data analysis techniques in certain dissertations based on Route B: Generalisation and Route C: Extension, the fact that these are clearly set out in the original study does make it much easier to effectively execute your dissertation.
Again, whilst there are a number of challenges to taking on a replication-based dissertation, four of the main challenges reflect: (a) convincing your supervisor that the contribution your dissertation will make will be sufficient; (b) an unclear research strategy; (c) critically analysing the literature; and (d) getting to grips with data analysis. Each of these challenges is discussed in turn:
Replication is sometimes thought of as a dirty word in research because it is closely associated with duplication (i.e., Route A: Duplication), which is often not considered to be a sufficient contribution, whether you are a student performing a dissertation, or even a seasoned academic. However, Route B: Generalisation and Route C: Extension are much more than just replication, and can offer a useful contribution to the literature, especially at the undergraduate and master's dissertation level. Whilst we strongly recommend that you present a dissertation proposal to your supervisor to gain their support, we believe that these two routes could lead to dissertations of a high standard. Therefore, it is always best to be careful when using the word replication when discussing a dissertation proposal with your supervisor. You will need to stress (a) why the research needs to be replicated (i.e., our Justifications for replication section), and (b) what you are contributing in addition to the original study (i.e., the generalisations or extensions that you plan to make).
To be blunt, if the research strategy that was used in the study you want to replicate is not clear (i.e., everything from the research design to the sampling strategy, data analysis techniques, etc.), we would more often than not recommend that you ditch the study and move on to another one. Yes, you may be able to ask the authors of the study for clarification, but if you cannot get easy access to the measurement procedures that were used (e.g., the questionnaire, including the measures that were used for each question), or the original data in the case of Route A: Duplication, you?re just making life very difficult for yourself from the start. Remember that one of the major benefits of replication-based dissertations is the guidance that you get from the study you want to replicate.
Generalisation-based replications, and even more so, extension-based replications require a very critical review of the literature. Whilst the original study may provide you with a good starting point in terms of the literature you need to read, you cannot simply take the literature review from the original study and summarise it. Unless the study you want to replicate was very recently published, there is likely to be a number of more recent journal articles that criticise and/or support the original study, but also articles that may question some of the fundamental assumptions that were made in this study. In addition, you will need to use the literature to build a case for the type of generalisation or extension you are making. Whilst we help show you how to do this within the Literature Review section of Lærd Dissertation, it is a challenge to critically analyse the literature in this way. However, we should point out that it will probably be much easier to do in replication-based dissertations compared with Route #3: Theory-driven dissertations. Nonetheless, if you feel that you may struggle to analyse the literature critically, you could always consider Route #2: Data-driven dissertations, where there is far less emphasis on the literature.
Even though we acknowledge that the data analysis stage of your dissertation will likely be a challenge in replication-based dissertations, it may be more challenging for Route #3: Theory-driven dissertations, and a lot more challenging for Route #2: Data-driven dissertations. The key to managing this challenge is selecting a study to replicate where you think that you could replicate the data analysis techniques used. Since it is unlikely you will know how to do this at this stage of the dissertation process, we help you understand what to think about when making these decisions. We also have extensive articles in the Data Analysis section of Lærd Dissertation that will show you how to select, analysis, and write up many statistical tests.
Having read through these benefits and challenges, again, think of your own situation, interests, and skill sets, and ask yourself: Is Route #1: Replication-based dissertations right for me? If you think it might be, learn how Lærd Dissertation can help in STEP FOUR.