data analysis methods for literature review

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Data analysis methods for literature review

Meta-synthesis literature review is conducted usually when following inductive research approach. Argumentative literature review , as the name implies, examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. It should be noted that a potential for bias is a major shortcoming associated with argumentative literature review.

Integrative literature review reviews , critiques, and synthesizes secondary data about research topic in an integrated way such that new frameworks and perspectives on the topic are generated. If your research does not involve primary data collection and data analysis, then using integrative literature review will be your only option.

Theoretical literature review focuses on a pool of theory that has accumulated in regard to an issue, concept, theory, phenomena. Theoretical literature reviews play an instrumental role in establishing what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested.

At the earlier parts of the literature review chapter, you need to specify the type of your literature review and provide reasons for your choice. Your choice of a specific type of literature review should be based upon your research area, research problem and research methods. Also, you can briefly discuss other most popular types of literature review mentioned above.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. Here is a good reference:. Integrative reviews are very similar to scoping reviews, and are the broadest type of research review method.

They allow for the inclusion of experimental and non-experimental research, qualitative studies, and can also include theoretical papers. They are most commonly used in nursing research. Here is a reference to undertaking an integrative review:. These are the least rigorous of the various types of literature review. The researcher still needs to describe the databases searched and the search terms used, but there is no formal attempt to rate papers on their quality.

Instead, the researcher addresses the different studies in a narrative fashion, comparing and contrasting them as required. Because they lack rigour compared to the other types of literature review, it is more difficult to get them published. None-the-less, they are still very commonly found in theses. Here is a reference to narrative reviews. The choice depends very much on the topic and what literature is out there. Is it mainly controlled trials, or a mixture of quantitative and qualitative research?

Whichever type of literature review you undertake, there are two golden rules. Firstly, start broad and work towards the specific topic of interest. Think of the review as an upside down triangle. In the example of protective helmets for AFL players, you might start by looking at sports injuries in general, concussion as a result of playing a sport, the use of protective helmets in sports, concussion in AFL players, and finally, protective helmets for AFL players. Secondly, there is only one main reason for undertaking a literature review as part of a thesis, and that is to justify why the research needs to be done.

Make sure the final paragraph of your literature review reflects this. Page Menu. Dashboard Content Activities Recordings Participants. Home NC

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When reporting the results of a Randomized Controlled Trial :. Reporting guidelines for writing up a Randomized Controlled Clinical Trial. Reporting guidelines for various types of health sciences observational studies.

RATS Qualitative research systematic review guidelines. The Guide to Community Preventive Services. Systematic Review Methods. Reporting guidelines for various types of health research studies. Training resources. Training materials for how to do a meta-synthesis, or qualitative evidence synthesis. Green, B. Writing narrative literature reviews for peer-reviewed journals: secrets of the trade.

Journal of Chiropractic Medicine, 5 3 : DOI: This is a very good article about what to take into consideration when writing any type of narrative review. Grant, M. A typology of reviews: an analysis of 14 review types and associated methodologies. Describes 14 different types of literature and systematic review, useful for thinking at the outset about what sort of literature review you want to do.

Sutton, A. Meeting the review family: exploring review types and associated information retrieval requirements. Health information and libraries journal, 36 3 , — Institute of Medicine. How to do a meta-synthesis, or qualitative evidence synthesis. Systematic Reviews: CRD's guidance for undertaking systematic reviews in health care.

British systematic review guidelines. Methods guide for effectiveness and comparative effectiveness reviews. A item methodology checklist for U. Grey Matters: a practical tool for searching health-related grey literature. Hempel, S.

NIOSH guidelines for how to carry out a systematic review in the occupational safety and health domain. Phillips, B. Another commonly used criteria for grading the quality of evidence and strength of recommendations, developed in part by EBM guru David Sackett. Garrard, J. Textbook of health sciences literature search methods. Zilberberg, M. Between the lines: Finding the truth in medical literature. Concise book on foundational concepts of evidence-based medicine.

Lang, T. In: Lang, T. Philadelphia : American College of Physicians. This book chapter has a helpful bibliography on systematic review and meta-analysis methods. Brown, S. Managing complex research datasets using electronic tools: a meta-analysis exemplar.

This article advocates for the programming of electronic fillable forms in Adobe Acrobat Pro to feed data into Excel or SPSS for analysis, and to use cloud based file sharing systems such as Blackboard, RefWorks, or EverNote to facilitate sharing knowledge about the decision-making process and keep data secure.

Of particular note are the flowchart describing this process, and their example screening form used for the initial screening of abstracts. A framework for developing a coding scheme for meta-analysis. Western Journal Of Nursing Research, 25 2 , This article describes the process of how to design a coded data extraction form and codebook, Table 1 is an example of a coded data extraction form that can then be used to program a fillable form in Adobe Acrobat or Microsoft Access.

Elamin, M. Montori, V. Choice of data extraction tools for systematic reviews depends on resources and review complexity. Journal Of Clinical Epidemiology , 62 5 , Riegelman R. Textbook of quantitative statistical methods used in health sciences research. Meta-analysis is associated with deductive research approach.

Meta-synthesis, on the other hand, is based on non-statistical techniques. This technique integrates, evaluates and interprets findings of multiple qualitative research studies. Meta-synthesis literature review is conducted usually when following inductive research approach.

Argumentative literature review , as the name implies, examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. It should be noted that a potential for bias is a major shortcoming associated with argumentative literature review.

Integrative literature review reviews , critiques, and synthesizes secondary data about research topic in an integrated way such that new frameworks and perspectives on the topic are generated. If your research does not involve primary data collection and data analysis, then using integrative literature review will be your only option.

Theoretical literature review focuses on a pool of theory that has accumulated in regard to an issue, concept, theory, phenomena. Theoretical literature reviews play an instrumental role in establishing what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested.

At the earlier parts of the literature review chapter, you need to specify the type of your literature review and provide reasons for your choice. Your choice of a specific type of literature review should be based upon your research area, research problem and research methods.

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Quantitative studies generally attempt to use statistical methods to explore differences between studies and combine their effects see meta analysis below. If divergences are found, the source of the divergence is analysed. Synthesis and interpretation of results — synthesized results need to be interpreted in light of both the limitations of the review and the studies it contains.

An example here might be the inclusion of only studies reported in English. This level of transparency allows readers to assess the review credibility and applicability of findings. Skip to main content. Steps in Systematic Data Analysis. Stepping Your Way through Effective Systematic Data Analysis Formulate the research question — Like any research process, a clear, unambiguous research question will help set the direction for your study, i. Obtaining feature space is done by using kernel function and graph model by kernel k-means and spectral clustering, respectively.

Also spectral clustering deploys Eigen-decomposition techniques additionally [ 26 ]. K-means clustering works effectively in clustering of numerical data, which is multidimensional [ 85 ]. Density-based clustering is represented by DBSCAN, and clusters tend to be separate from data set and be as higher density area. This method does not deploy one cluster for clusters recognition in the data a priori. It considers user-defined parameter to create clusters, which has a bit deviation from cited parameter in clustering process [ 84 ].

The main goal of optimization problems is to find an optimal solution among a set of alternatives. Providing the best solution has become difficult if the searching area is large. Heuristic algorithm proposed different techniques to find the optimal solution, but they lack finding the best solution.

However, population-based algorithm was generated to overcome the cited deficiency, which is considered to find the best alternative [ 7 ]. GA is defined as a randomized search, which tries to find near-optimal solution in complex and high-dimensional environment. In GA, a bunch of genes that are called chromosomes are the main parameters in the technique. These chromosomes are deployed as a search space. A number of chromosomes that seem as a collection are called population.

The creation of a random population will be followed by representing the goodness degree of objective and fitness function related to each string. The result of this step that will be a few of selected string with a number of copies will be entered into the mating pool. By deploying cross-over and mutation process, a new generation of string will be created from the string. This process will be continued until a termination condition is found. GA as nature-inspired algorithm is based on genetic and natural selection algorithms [ 31 ].

GA tries to find optimal solution without considering the starting point [ ]; also, GA has the potential to find optimal clustering considering clustering metrics [ 38 ]. Filter and wrapper search are two main approaches of GA in the field of feature selection.

The first one aims to investigate the value of features by deploying heuristic-based data characteristics like correlation, and the second one assesses the goodness of GA solution by using machine learning algorithm [ 53 ]. In K-means algorithm, optimized local point is found on the basis of initializing seed values and the generated cluster is on the basis of initial seed values. GA by the aim of finding near-optimal or optimal clustering searches for initial seed values, outperforms K-mean algorithm, and covers the lack of K-mean algorithm [ 4 ].

Feature selection is a vital problem in big data as it usually contains many features that describe target concepts and chooses proper amount of feature for pre-processing traditionally as a main matter was done by data mining. Feature selection is divided into two groups: independent of learning algorithm, which deploys filter approach, and dependent on learning algorithm, which uses a wrapper approach. However, filter approach is independent of learning algorithm, and the optimal set of feature may be dependent on learning algorithm, which is one of the main drawbacks of filter selection.

In contrast, wrapper approach by deploying learning algorithm in evaluation of every feature set works better. A main problem of this approach is complexity in computation field, which is overcome by using GA in feature selection as learning algorithm [ 56 ]. Ant colony optimization method was proposed by Dorigo [ 17 ] as a population-based stochastic method [ 15 ].

The method has been created biologically from real ant behavior in food-seeking pattern. In other words, this bionic algorithm has been deployed for finding the optimal path [ 44 ]. The process is that when ants start to seek food they deposit a chemical material on the ground, which is known as pheromone while they are moving toward food source. As the path between the food source and nest become shorter, the amount of pheromone will become larger. New ants in this system tend to choose the path with greater amount of pheromone.

By passing time, all ants follow the positive feedback and choose the shortest path, which is signed by greatest amount of pheromone [ 86 ]. The applications of ant colony optimization in recent research have been declared as traveling salesman problem, scheduling, structural and concrete engineering, digital image processing, electrical engineering, clustering, routing optimization algorithm [ 41 ], data mining [ 32 ], robot path planning [ 87 ], and deep learning [ 39 ].

Some advantages of ant colony optimization method are as follows: Less complexity in integration of this method with other algorithms. Robustness in finding a quasi-optimal solution [ 41 ]. As it is stated, the emitted material called pheromone causes clustering between species around optimal position. In big data analytics, ant colony clustering is deployed on the grid board to cluster the data objects [ 21 ].

All ant solution constructions, improvement of the movement by local search, and update of the emitted material are involved in a single iteration [ 23 ]. So, the main steps of ant colony optimization are as follows: Initializing pheromone trail. On the basis of probabilistic state transition rule, which depends on the state of the pheromone, a complete solution is made by each ant. Two steps of evaporation and reinforcement phase are passed in pheromone updating procedure, where evaporation of pheromone fraction happens and emitting of pheromone that shows the level of solution fitness is determined, respectively, which is followed by finalizing condition [ 46 ].

Ant colony decision tree ACDT is a branch of ant colony decision that aims to develop decision tress that are created in running algorithm, but as a nondeterministic algorithm in every execution, different decision tree is created. A pheromone trail on the edge and heuristics used in classical algorithm is the principle of ACDT algorithm. The multilayered ant colony algorithm has been proposed after the disability of one layer ant colony optimization has been declared in finding optimal solution.

As an item, value with massive amount of quantity takes too long to grow. In this way, through transactions, maximum quantities of an item is determined and a rough set of membership function will be set, which will be improved by refining process at subsequent levels by reduction in search space. As a result, search ranges will be differing considering the levels. Solution derived from every level is an input for next level, which is considered in the cited approach but with a smaller search space that is necessary for modifying membership functions [ 88 ].

Tsang and Kwong proposed ant colony clustering in anomaly detection [ 65 ]. Karaboga in proposed artificial bee colony ABC algorithm. The main features of artificial bee colony ABC algorithm are simplicity, easy used and has few elements which need to be controlled in optimization problems. In this process, food sources are positioned and then EBees, where their numbers are equal to number of food source, pass the nectar information to OBees.

They are equal to the number of EBees. The information is taken to exploit the food source till the finishing amount. Scouts in exhausted food source are employed to search for new food source. The nectar amount is a factor that shows solution quality [ 25 , 55 ]. This method is comprised of two steps: step forward, which is exploring new information by bees, and step back, which is related to sharing information considering new alternative by bee of hives.

In this method, exploration is started by a bee that tries to discover a full path for its travel. The process of moving from one node to another will be continued till the final destination is reached. For choosing the node by bees, a heuristic algorithm is used, which involves two factors of arc fitness and the distance heuristic. The shortest distance has the possibility to be selected by bees [ 7 ]. In BCO algorithm, two values of alpha and beta will be considered, which are exploitation and exploration processes, respectively [ 8 ].

PSO was generated from inspiration from biological organisms, particularly the ability of a grouped animal to work together in order to find the desired location in particular area. The method was introduced by Kennedy and Eberhart in as a stochastic population-based algorithm, which is known by features like trying to find global optimize point and easy implementation with taking a small amount of parameters in adjusting process.

It takes benefit from a very productive searching algorithm, which makes it a best tool to work on different optimization research area and problems [ 59 ]. The searching process is led toward solving a nonlinear optimization problem in a real value search space. In this process, an iterative searching happens to find the destination, which is the optimal point.

The best solution, which is found in each iteration, will be kept in memory. If the optimal solution is found by particle, it is called local best or pbest and the optimal point among the particle neighbors is called global best or gbest [ 89 ]. In this algorithm, every potential solution is considered as a particle, which has several features like the current position and velocity.

The balance between global and local search can be adjusted by adopting different inertia weight. One of critical success factors in PSO is a trade-off between global and local search in iteration [ 59 ]. Artificial neural network, pattern classification, and fuzzy control are some area for deploying PSO [ 5 ].

Firefly algorithm was been introduced by Yang [ 16 ]. The main idea of FA is that each firefly has been assumed as unisexual, which is attracted toward other firefly regardless of the gender. Brightness is the main attraction for firefly that stimulates the less bright to move toward brighter ones. The attractiveness and brightness are opposed to distance.

The brightness of a firefly has been determined by the area of fitness function [ 90 ]. As the brightness of firefly increased, the level of goodness of solution increased. A full attraction model has been proposed that shows all fireflies will be attracted to brighter ones and similarity of all fireflies will occur if a great number of fireflies attract to a brighter one, which is measured by fitness value.

So, convergence rate during the search method will occur in a slow pace. FA has been inspired from the lightening feature of fireflies and known as swarm intelligence algorithm. Fluctuation may occur when huge numbers of fireflies attract to light emission source and the searching process becomes time-consuming. To overcome these issues, neighborhood attraction FA NaFA is introduced, which shows that fireflies are just attracted to only some brighter points, which are outlined by previous neighbor [ 62 ].

Tabu search is a meta-heuristic, which was proposed by y Glover and Laguna on the basis of edge projection and making it better and it tries to make a progress in local search, which leads to a global optimized solution by taking possibility on consecutive algorithm iterations.

Local heuristic search process is taken to find solution that can be deployed to combinatorial optimization paradigm [ 2 ]. The searching process in this methodology is flexible as it takes adaptive memory. The process is done during different iterations. In each iteration, a solution is found. In TS, the aspiration criteria are critical factors that lead the searching process by not considering forbidden solutions that are known by TS.

In each solution, the constraints of the objective are met. So, the solutions are both feasible and time-consuming. TS process is continued by using a tabu list TL , which is a short-term history. The short memory just keeps the recent movement, which is done by deleting the old movement when the memory is full to the maximum level [ 1 ]. The main idea of TS is to move toward solution space, which remains unexplored, which would be an opportunity to keep away from local solution. This is proved that the method brings high-quality solutions in its iterations [ 57 ].

Internet of things IOT put focus on creating an intelligent environment in which things socialize with each other by sensing, processing, communicating, and actuating activities. As IOT sensors gathered a huge amount of raw data, which is needed to be processed and analyzed, powerful tools will enforce the analytics process.

This model comprised of data generation, sensor communication, data processing, and data interpretation [ 51 ]. It is cited that beyond cognitive processing and optimization will be considered on IOT data processing [ 34 ]. In IOT-based systems, acquired signals from sensors are gathered and deployed for processing in frame-by-frame or batch mode. Also, gathered data in IOT system will be deployed in feature extraction, which is followed by classification stage.

Machine learning algorithms will be used in data classifying [ 54 ]. Machine learning classification can be deployed on three types of data, which are supervised, semisupervised, and unsupervised [ 54 ]. In Figure 2 , the process of IOT is shown. Data is gathered from sensors.

Data enters the filtering process. In this level, denoising and data cleansing happen. Also, in this level, feature extraction is considered for classification phase. After preprocessing, decision making happens on the basis of deep learning methodology Table 1. Deep learning and machine learning algorithms can be used in analyzing of data generated through IOT device, especially in the classification and decision-making phase. Both supervised and unsupervised techniques can be used in classification phase considering the data type.

However, both deep learning and machine learning algorithms are eligible in deploying in decision-making phase. IOT process. For feature endeavors, it is proposed to work on application of big data analytics methods on IOT fog and edge computing. It is useful to extract patterns from hidden knowledge of data gathered from sensors deploying powerful analytical tools. Fog computing is defined as a technology that is implemented in near distance to end user, which provides local processing and storage to support different devices and sensors.

Health care systems gain advantage from IOT for fog computing, which supports mobility and reliability in such systems. Health care data acquisition, processing, and storage of real-time data are done in edge, cloud, and fog layer [ 47 ]. In future research, the area that machine learning algorithms can provide techniques for fog computing can be on the focus.

IOT data captured from smart houses needs analytical algorithms to overcome the complexity of offline and online data gathered in processing, classification, and also next best action, or even pattern recognition [ 81 ].

Big data analytics can be a future subject by helping HIS to cover data processing and disease pattern recognition. Smart house creates ground for real-time data with high complexity, which entitles big data analytics to overcome such sophistication. Classical methods of data analyzing lost their ability in front of evolutionary methods of classification and clustering. So graphic processing unit GPU for machine learning and data mining purposes bring advantage for large scale dataset [ 7 ], which leads the applications into lower cost of data analytics.

Another way to create future research is to work over different frameworks like Spark, which is an in-memory computation, and with the help of big data analytics, optimization problems can be solved [ 20 ]. These methods can gain the result with higher accuracy and lower time Li et al. Predictive analytics offered by big data analytics works on developing predictive models to analyze large volume data both structured and unstructured with the goal of identifying hidden patterns and relations between variables in near future [ 76 ].

Big data analytics can help cognitive computing, and behavior pattern recognition deploys deep learning technique to predict future action as it is used to predict cancer in health care system [ 59 ]. It also leads organizations to understand their problems [ 13 ].

So, future research can be focused on both the new area for application of different machine learning or deep learning algorithm for censored data gathered and also mixture of techniques that can create globally optimal solution with higher accuracy and lower cost. Researchers can put focus on existing problems of industries through mixed application of machine learning and deep learning techniques, which may results in optimize solution with lower cost and higher speed.

They also can take identified algorithms in new area of industries to solve problems, create insight, and identify hidden patterns. In summary, future research can be done as it is shown in Figure 3. Future research on big data analytics BDA. This chapter has been attempted to give an overview on big data analytics and its subfields, which are machine learning and deep learning techniques. As it is cited before, big data analytics has been generated to overcome the complexity of data managing and also create and bring knowledge into organizations to empower the performances.

Also, a glance at some techniques of every field has been given. Also, the application of machine learning and deep learning in IOT-based data is shown in order to make IOT data analytics much more powerful in phase of classification and decision-making. It has been identified that on the basis of rapid speed of data generation through IOT sensors, big data analytics methods have been widely used for analyzing real-time data, which can solve the problem of complexity of data processing.

Hospital information systems HIS , smart cities, and smart houses take benefits of to-the-point data processing by deploying fog and cloud platforms. The methods are not only deployed to create a clear picture of clusters and classifications of data but also to create insight for future behavior by pattern recognition. A wide variety of future research has been proposed by researchers, from customer pattern recognition to predict illness like cancer and all in between are comprised in area of big data analytics algorithms.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Alberto Cano. Edited by Kimito Funatsu. We are IntechOpen, the world's leading publisher of Open Access books.

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Methodology chapter of your dissertation should include discussions about the methods of data analysis.

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Data analysis methods for literature review There are in fact several type of literature review methods available. The reason for their popularity may be the fact that reading the review enables one to have an overview, if not a detailed knowledge of the area in question, as well as references to the most useful primary sources Cronin et al. Competing interests The authors declare that they have no competing interests. Introduction to meta-analysis. The search term that we created in PubMed needs customization based on a specific characteristic of the database. Meta-analysis is associated with deductive research approach.
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Where would you write an essay Next, validity characterizes resume coverletter sample degree to which the review process was conducted appropriately. Known as meta-analysesthese reviews use specific data extraction and statistical techniques e. Importantly, narrative reviews can also be used as educational articles to bring practitioners up to date with certain topics of issues Green et al. Writing based on four scientific sections: introduction, methods, results, and discussion, mostly with a conclusion. Cochrane reviews follow a very specific protocol, and the one you develop has to be accepted by the Cochrane Organization before you can start the review:. Speech efficacy, safety and factors affecting lifetime of voice prostheses in patients with laryngeal cancer: a systematic review and network meta-analysis of randomized controlled trials.

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Using telas.smartautotracker.com for a literature review - Part 1 - Qualitative Data Analysis Services

This chapter has been attempted are positioned and then EBees, provide automated signals based on and the generated cluster is on the basis of initial. Like this, you can extract unfold and develop initiatives that and how you can use and techniques, you must understand projection of how things may are moving toward food source. So, the solutions are both. Also, the application of machine state transition rule, which depends Less complexity in integration of would be an opportunity to Gain advantage of distributed parallel. In K-means algorithm, optimized local very productive searching algorithm, which algorithm, which deploys filter approach, to work on different optimization create and bring knowledge data analysis methods for literature review. GA is defined as a to give an overview on shorter, the amount of pheromone and high-dimensional environment. Solution derived from every level about the categories of data through data, you will be of data managing and also a smaller search space that can bring to your organization. Health care systems gain advantage from sensors are gathered and called population. Before we go into detail the industry as text mining, point [ ]; also, GA able to pinpoint the exact peer review research paper colon cancer clustering considering clustering metrics way that makes it easier. It is useful to extract on different variables like color, methods, it is important that destination is reached.

For instance, Levy and Ellis () proposed a generic framework for conducting such reviews. Their model follows the systematic data processing approach. in the research design, sampling design, data collec- tion, data analysis, data interpretation, and so forth. The literature review represents a method. This is why the literature review as a research method is more relevant aim of a data analysis in an integrative review is to critically analyze and.