Types of research methods and disciplines A dissertation is an extended piece of writing based on comprehensive reading and research, written by an academic scholar at an undergraduate, masters or post graduate level. In some cases, a dissertation is referred to an academic research document written at PhD level, while a Thesis may be one which is written by an academic at Masters or Undergraduate level. However the opposite is also true in other cases. Etymology[edit] The word ‘dissertation’ was derived from the Latin word dissertātiō which means ‘discourse’ or ‘path. Types of Research[edit] There are two types of research which can be done to develop a thesis or dissertation: Practical Research: The practical approach consists of the empirical study of the topic under research and chiefly consists of hands on approach. Types of Research Method[edit] Descriptive/Qualitative[edit] This type of research methods involve describing in details specific situation using research tools like interviews, surveys, and observations.[3] [edit]
Social psychology Social psychologists therefore deal with the factors that lead us to behave in a given way in the presence of others, and look at the conditions under which certain behavior/actions and feelings occur. Social psychology is concerned with the way these feelings, thoughts, beliefs, intentions and goals are constructed and how such psychological factors, in turn, influence our interactions with others. In addition to the split between psychology and sociology, there has been a somewhat less pronounced difference in emphasis between American social psychologists and European social psychologists. As a broad generalization, American researchers traditionally have focused more on the individual, whereas Europeans have paid more attention to group level phenomena (see group dynamics).[3][page needed] History[edit] Intrapersonal phenomena[edit] Attitudes[edit] Persuasion[edit] The topic of persuasion has received a great deal of attention in recent years. Social cognition[edit] Self-concept[edit]
Meta-analysis In statistics, meta-analysis comprises statistical methods for contrasting and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies.[1] Meta-analysis can be thought of as "conducting research about previous research." In its simplest form, meta-analysis is done by identifying a common statistical measure that is shared between studies, such as effect size or p-value, and calculating a weighted average of that common measure. This weighting is usually related to the sample sizes of the individual studies, although it can also include other factors, such as study quality. The motivation of a meta-analysis is to aggregate information in order to achieve a higher statistical power for the measure of interest, as opposed to a less precise measure derived from a single study. History[edit] Advantages[edit] [edit]
What is the new sociology of Ideas ? A Discussion with Charles Camic and Neil Gross - Transeo Review Humanities are not so common an object of investigation for social scientists. Other disciplines (intellectual history, political science or even philosophy) tend to challenge the accounts produced by sociologists. Moreover, the sophistication of disciplines such as philosophy or economic analysis can sometimes be a barrier for sociologists working on them. Could you briefly describe your personal trajectory, your education and past interests, and the reasons why you turned to the sociology of ideas ? Charles Camic : Although I “turned” about 10 years ago to using the expression “sociology of ideas” to describe my work, I’ve actually been doing research in this vein onward from my time in graduate school. When I was a sociology graduate student at the University of Chicago in the mid-1970s, my initial interests were social theory and the sociology of education. Neil Gross : You coined the phrase “new sociology of ideas” to describe the research field your works aim to contribute to.
Scientometrics Scientometrics is the study of measuring and analysing science research. In practice, scientometrics is often done using bibliometrics which is a measurement of the impact of (scientific) publications. Modern scientometrics is mostly based on the work of Derek J. de Solla Price and Eugene Garfield. See also[edit] Further reading[edit] Derek J. de Solla Price, Little Science, Big Science (New York, 1963)G. External links[edit]
Cousins of Neanderthals Left DNA in Africa, Scientists Report The geneticists reached this conclusion, reported on Thursday in the journal Cell, after decoding the entire genome of three isolated hunter-gatherer peoples in Africa, hoping to cast light on the origins of modern human evolution. But the finding is regarded skeptically by some paleoanthropologists because of the absence in the fossil record of anything that would support the geneticists’ statistical calculations. Two of the hunter-gatherers in the study, the Hadza and Sandawe of Tanzania, speak click languages and carry ancient DNA lineages that trace to the earliest branchings of the human family tree. The third group is that of the forest-dwelling pygmies of Cameroon, who also have ancient lineages and unusual blood types. The geneticists, led by Joseph Lachance and Sarah A. Among the DNA sequences special to pygmies, Dr. The genomes of the pygmies and the Hadza and Sandawe click-speakers contained many short stretches of DNA with highly unusual sequences. Dr. Dr.
Domain analysis In software engineering, domain analysis, or product line analysis, is the process of analyzing related software systems in a domain to find their common and variable parts. It is a model of wider business context for the system. The term was coined in the early 1980s by James Neighbors.[1][2] Domain analysis is the first phase of domain engineering. Domain analysis produces domain models using methodologies such as domain specific languages, feature tables, facet tables, facet templates, and generic architectures, which describe all of the systems in a domain. The products, or "artifacts", of a domain analysis are sometimes object-oriented models (e.g. represented with the Unified Modeling Language (UML)) or data models represented with entity-relationship diagrams (ERD). In information science, the term "domain analysis" was suggested in 1995 by Birger Hjørland and H. Domain analysis techniques[edit] References[edit] Jump up ^ Neighbors, J.M. See also[edit]
Co-citation Figure visualizing co-citation on the left and a refinement of co-citation, Co-citation Proximity Analysis (CPA) on the right. Co-citation, like Bibliographic Coupling, is a semantic similarity measure for documents that makes use of citation relationships. Co-citation is defined as the frequency with which two documents are cited together by other documents.[1] If at least one other document cites two documents in common these documents are said to be co-cited. The more co-citations two documents receive, the higher their co-citation strength, and the more likely they are semantically related.[1] The figure to the right illustrates the concept of co-citation and a more recent variation of co-citation which accounts for the placement of citations in the full text of documents. The figure's right image shows a citing document which cites the Documents 1, 2 and 3. Over the decades, researchers proposed variants or enhancements to the original co-citation concept. Considerations[edit]
Factor analysis Factor analysis is related to principal component analysis (PCA), but the two are not identical. Latent variable models, including factor analysis, use regression modelling techniques to test hypotheses producing error terms, while PCA is a descriptive statistical technique.[1] There has been significant controversy in the field over the equivalence or otherwise of the two techniques (see exploratory factor analysis versus principal components analysis).[citation needed] Statistical model[edit] Definition[edit] Suppose we have a set of observable random variables, with means Suppose for some unknown constants and unobserved random variables , where , we have Here, the are independently distributed error terms with zero mean and finite variance, which may not be the same for all . , so that we have In matrix terms, we have If we have observations, then we will have the dimensions , and . denote values for one particular observation, and matrix does not vary across observations. and are independent. . or .
Pathfinder network Several psychometric scaling methods start from proximity data and yield structures revealing the underlying organization of the data. Data clustering and multidimensional scaling are two such methods. Network scaling represents another method based on graph theory. Here is an example of an undirected Pathfinder network derived from average similarity ratings of a group of biology graduate students. The Pathfinder algorithm uses two parameters. (1) The q parameter constrains the number of indirect proximities examined in generating the network. With ordinal-scale data (see level of measurement), the r-parameter should be infinity because the same PFnet would result from any positive monotonic transformation of the proximity data. Essentially, Pathfinder networks preserve the shortest possible paths given the data so links are eliminated when they are not on shortest paths. References[edit] Schvaneveldt, R. A shorter article summarizing Pathfinder networks: Schvaneveldt, R.