What is the difference between ethnomethodology and grounded theory?
Ethnomethodology and grounded theory are examples of qualitative research methods. Researchers that adopt an ethnomethodology approach engage directly with the study subjects. In sociology, ethnomethodology researchers adopt an overtly or covertly approach, and this enables researchers to acquire primary information directly from the participants. With ethnomethodology, all possible information is collected by the researcher directly from the subjects (McCreaddie & Payne, 2010).
The principle behind the use of ethnomethodology is the desire to encounter an alien environment, living style, or language. A researcher learns the meaning and importance of the language as he interacts with the study participants. In ethnomethodology, knowledge is considered as an extension of common sense. Our behaviors and actions are, therefore, an extension of common sense that we have acquired. Ethnomethodology recognizes the need to appreciate the strengths and weaknesses of the study subjects and correct the errors identified (Gentles & Jack, 2014).
Grounded theory is based on the desire to develop a single reference theory that can be used to describe the topic under study. Unlike in ethnomethodology, all data are included and this helps in the process of improving the target theory. Grounded theory embraces the use of interviews, observations, and other secondary approaches of collecting data as opposed to living with the study subjects. Ethnomethodology does not embrace constant comparison as witnessed in grounded theory.
Though the grounded theory is common, it has a number of challenges that are not common with ethnomethodology. For example, the process of sampling, data collection, and analysis is more complex as compared to ethnomethodology. Ethnomethodology and grounded theory are all based on an interpretive approach that is guided by a desire to interrogate actual events. To achieve this, the two methods actively engage the research subjects through active observation or interviews. In both approaches, data is collected and analyzed without the involvement of preconceived feelings of the researcher (Aldiabat & Navenec, 2011).
What is a time series design?
Time-series design is a quasi-experimental approach in which measurements are made periodically within the study group. The measurements are made before and after the intervention or control has been administered. By taking measurements at different points during the study, different trends are measured. This explains the use of the term ‘trend design’ when describing the use of time series in cross-sectional studies (Glass & Gottman, 2008).
Time-series design is used in different circumstances by researchers. For example, in situations where measures should be taken within specific time periods, time series design is preferred. For example, the impact of a new antiretroviral therapy can be measured after a defined period. By measuring the level of CD4 cells, doctors can be able to understand the progress of the patient. Following an administration of the new ART, a time series design can be used to measure the level of CD4 cells after every four months (Anaby & Lal, 2014).
Time-series design involves different processes that are dependent on the subject of study. To examine time series manually, line chart processes are employed. In this situation, cases and events are classified against time to provide value. For example, the level of tuberculosis incidence in the United States can be measured using a line chart process. Prediction and forecasting is the second process of time series design. This process allows for the use of different statistical data analysis methods such as SPSS and SAS.
What is the difference between quantitative analysis and qualitative analysis?
Qualitative and quantitative analyses are two common analytical approaches used in study designs. The qualitative analysis provides a complete and detailed description of the variables being interrogated by the study. Values are not given finite classification, and this allows for a fine distinction to be drawn. Research is reliant on human language, information, and understanding. However, ambiguities affect the accuracy of such information. With qualitative analysis, ambiguities can easily be recognized and eliminated (Arghode, 2012).
Quantitative analysis is used for the evaluation of finite and countable variables. It, therefore, allows for the construction of various statistical models that can be analyzed. While qualitative analysis is isolated to a single population, quantitative variable allows for comparison and generalization of the results obtained. Quantitative analysis requires the presence of data that can be analyzed while qualitative analysis handles information that is immeasurable (Arghode, 2012).
As a result, information analyzed quantitatively can be reported through numbers. For example, an organization may be forced to make an analysis on the customer preferences, the nature of complaints made, and the implication on the organization’s reputation. Such data can only be analyzed qualitatively as they are non-quantifiable (Arghode, 2012).
What is a test of statistical significance and why is it important?
Test of statistical significance is the probability law used to describe the chance of achieving certain results. Statistical significance can be defined as the probability of obtaining results within the level of significance given that the null hypothesis is accepted. In research, two variables are developed which can be hypothesized using null and alternative hypotheses (Mbengué, 2010).
It seeks to determine the chance that a given relationship between variables is present. In the event that a relationship exists between the variables, statistical significance describes the strength of the relationship. Test of statistical significance uses the normal distribution curve and the probability theory to test the relationship between two variables (Mbengué, 2010).
Assumptions are, therefore, made in the process of designing the null and alternative hypotheses. If the chance of getting a certain outcome out of the event is minimal, the relationship can be accepted. Test of statistical significance is important in testing the hypothesis of a study. In research studies, null and alternative hypotheses are established which are tested by the data collected. Test of statistical significance enables researchers to either reject the null hypothesis or adopt the alternative hypothesis (Mayer, 2012).
Test of statistical significance is also used in the evaluation of the importance of the observed correlation coefficient and the sample regression coefficient. In situations where two sample means are available, a test of statistical significance can be used to determine the importance of the difference between the two.
What is the purpose and method of writing a literature review?
A literature review is a common practice when writing research papers. In modern research publications, a review of the literature is conducted to indicate the knowledge gap that will be supported by the current study. A literature review is a basis for developing the context of any research study. By providing the results and conclusion from previous studies, researchers demonstrate the level of significance of the chosen study topic. Apart from providing background information for the current study, the literature review also demonstrates that the current topic has not been interrogated previously (Aveyard, 2014).
A literature review can be conducted using different methods. For example, the study topic can be used to identify past studies published in a related area. For example, a paper on peer pressure can use the topic to identify previous publications. A literature review can also be conducted by identifying a problem and the solution that was identified by other studies. The cause-effect method is also commonly used in the process of reviewing the literature (Oliver, 2012).
References
Aldiabat, K., & Navenec, L. (2011). Clarification of the Blurred Boundaries between Grounded Theory and Ethnography: Differences and Similarities. Turkish Online Journal of Qualitative Inquiry, 2(3), 1-13.
Anaby, D., & Lal, S. (2014). Interrupted Time Series Design: A Useful Approach for Studying Interventions Targeting Participation. Physical & Occupational Therapy in Paediatrics, 34(4), 457-470. Web.
Arghode, V. (2012). Qualitative and Quantitative Research: Paradigmatic Differences. Global Education Journal, 12(4), 155-163.
Aveyard, H. (2014). Doing a Literature Review in Health and Social Care: A Practical Guide. Maidenhead: Open University Press.
Gentles, J., & Jack, M. (2014). A Critical Approach to Reflexivity in Grounded Theory. Qualitative Report, 19(44), 1-14.
Glass, G., & Gottman, M. (2008). Design and Analysis of Time-series Experiments. Charlotte, NC: Information Age Publishing.
Mayer, T. (2012). Ziliak and McCloskey’s Criticisms of Significance Tests: An Assessment. Econ Journal Watch, 9(3), 256-297.
Mbengué, A. (2010). Should we burn the statistical significance tests? Management, 13(2), 100-125.
McCreaddie, M., & Payne, S. (2010). Evolving Grounded Theory Methodology: Towards a discursive approach. International Journal of Nursing Studies, 47(6), 781-793. Web.
Oliver, P. (2012). Succeeding with Your Literature Review: A Handbook for Students. Maidenhead: Open University Press.