Random questions for exam Flashcards
Define the concept of validity
Validity is a concern with the integrity of the conclusions that are generated from a piece of research. There are different aspects of validity. See, in particular, measurement validity, internal validity, external validity, and ecological validity. When used on its own, validity is usually taken to refer to measurement validity.
Define the concept of reliability
Reliability is the degree to which a measure of a concept is stable.
What does the measure R2 tell you in the context of a regression analysis?
R-Squared (R² or the coefficient of determination) is a statistical measure in a regression model that determines the proportion of variance in the dependent variable that can be explained by the independent variable. In other words, r-squared shows how well the data fit the regression model (the goodness of fit).
What is grounded theory?
In its most recent incarnation, grounded theory has been defined as ‘theory that was derived from data, systematically gathered and analyzed through the research process. In this method, data collection, analysis, and eventual theory stand in close relationship to one another’ (Strauss and Corbin 1998: 12). Thus, two central features of grounded theory are that it is concerned with the development of theory out of data and the approach is iterative, or recursive, as it is sometimes called, meaning that data collection and analysis proceed in tandem, repeatedly referring back to each other. Grounded theory (see Key concept 22.3) has become by far the most widely used framework for analysing qualitative data.
What are the tools of grounded theory?
Tools of grounded theory:
theoretical sampling: A term used mainly in relation to grounded theory to refer to sampling carried out so that emerging theoretical considerations guide the selection of cases and/or research participants. Theoretical sampling is supposed to continue until a point of theoretical saturation is reached.
theoretical saturation: In grounded theory, the point when emerging concepts have been fully explored and no new theoretical insights are being generated. See also theoretical sampling.
coding: In quantitative research, codes act as tags that are placed on data about people or other units of analy- sis. The aim is to assign the data relating to each variable to groups, each of which is considered to be a category of the variable in question. Numbers are then assigned to each category to allow the information to be processed by the computer. In qualitative research, coding is the process whereby data are broken down into component parts, which are given names.
constant comparison: a procedure for evaluating qualitative data in which the information is coded and compared across categories, patterns are identified, and these patterns are refined as new data are obtained.
Describe the process of analytical induction or pattern-matching.
Analytic induction is an approach to the analysis of data in which the researcher seeks universal explanations of phenomena by pursuing the collection of data until no cases that are inconsistent with a hypothetical explanation (deviant or negative cases) of a phenomenon are found.
The experimental design involves a number standardized steps. Briefly outline these steps.
In what is known as the classical experimental design, two groups are established and this forms the basis for experimental manipulation of the independent variable. The experimental group, or treatment group, receives the treatment, and it is compared against the control group, which does not. The dependent variable is measured before and after the experimental manipula- tion, so that a before-and-after analysis can be conducted. Moreover, the groups are assigned randomly to their respective groups. This enables the researcher(s) to feel confident that any difference between the two groups is attributable to manipulation of the independent variable.
In order to capture the essence of this design, the following simple notation is employed:
Obs: An observation made in relation to the dependent variable; there may well be two or more observations, before (the pre-test) and after (the post-test) the experimental manipulation.
Exp: The experimental treatment (manipulation of the independent variable). No Exp refers to the absence of an experimental treatment and represents the experience of the control group.
T: The timing of the observations made in relation to the dependent variable.
Procedure (between subject design)
1. Random assignment of individuals to an experimental and control group
2. Pretest of both groups (measure the dependent variable)
3. The experimental group receives the experimental treatment (manipulation of the independent variable) while the control group doesn’t
4. Posttest of both groups (measure the dependent variable)
5. The difference between the experimental and control group’s score on the dependent variable is computed to see if the experimental treatment has made a significant difference
What is the ideal way of drawing a sample for a quantitative study? Why?
Probability sample: a sample that has been selected using random selection so that each unit in the population has a known chance of being selected. It is generally assumed that a representative sample is more likely to be the outcome when this method of selection from the population is employed. The aim of probability sampling is to keep sampling error (see below) to a minimum.
Describe cross-sectional design
Cross-Sectional Design is a research design that entails the collection of data on more than one case (usually quite a lot more than one) and at a single point in time in order to col- lect a body of quantitative or quantifiable data in connection with two or more variables (usually many more than two), which are then examined to detect patterns of association.
The cross-sectional design is often called a social survey design, but the idea of the social survey is so closely connected in most people’s minds with questionnaires and structured interviewing that the more generic-sounding term cross-sectional design is preferable. While the research methods associated with social surveys are certainly frequently employed within the context of cross-sectional research, so too are many other research methods, including structured observation, content analysis, official statistics, and diaries. All these research methods will be covered in later chapters, but in the meantime the basic structure of the cross-sectional design will be outlined.
Describe longitudinal design
Longitudinal research is a research design in which data are collected on a sample (of people, documents, etc.) on at least two occasions.
Explain the difference between cross-sectional design and longitudinal design.
Cross-sectional studies and longitudinal studies are two types of research designs used in empirical studies. The main difference between these two designs is in the way they measure data over time.
Cross-sectional studies collect data at a single point in time. In this design, a group of individuals are selected and data is collected from them at one time point. The aim of a cross-sectional study is to measure the prevalence or frequency of a certain phenomenon at a specific point in time. For example, a cross-sectional study might measure the prevalence of smoking in a specific population at a given time.
On the other hand, longitudinal studies collect data at multiple points in time. In this design, a group of individuals are selected and data is collected from them at two or more time points. The aim of a longitudinal study is to observe changes over time and to identify possible causes and effects of those changes. For example, a longitudinal study might track a group of individuals over a period of time to see how their smoking habits change and how this relates to their health outcomes.
In summary, cross-sectional studies measure data at a single point in time, while longitudinal studies measure data at multiple points in time to observe changes and identify potential causes and effects.
