King, Keohane, and Verba (1994) in “Designing Social Inquiry: Scientific Inference in Qualitative Research” attempt to unify political science under a single logic of inquiry based on quantitative regression analysis. While initially divisive even somewhat offensive to qualitative scholars, this debate culminated in greater scrutiny of qualitative methods and the delineation of the key advantages and limitations of both qualitative and quantitative methods. In the end, KKV’s attempt to unite the field provided an opportunity for advances and expansions in both quantitative and qualitative methodologies, greatly expanding and enhancing the comparativist’s methodological toolbox. Nevertheless, in trying to unite the discipline under a single …show more content…
“Descriptive inference is the process of understanding an unobserved phenomenon on the basis of a set of observations”(KKV 1994: 55). Descriptive inference has both a systematic component and nonsystematic component of the variance in the phenomena under study. Furthermore, descriptive inferences are judged based on objectives of unbiasedness, efficiency, and consistency (63) in assessing the estimates of the variation or …show more content…
The resulting inferences are only as good as the combination of these factors. Statistical analysis, however, can suffer from a lack of theoretical and conceptual underpinnings (Achen 2002: 424, Johnson 2006: 238). Minimalist definitions of a concept include the largest number of cases, but risk conceptual stretching, whereas maximalist definitions would include so many descriptive attributes, the number of cases dwindles. Statistical analysis tends to include the most cases possible and thus risks conceptual stretching (Sartori 1970, Munck and Verkuilen 2002). Statistical models can be underspecified and not include independent variables that impact the change in the dependent
To investigate this further I will discuss these assumptions and identify particular methods favoured in relation to two contrasting theories, ...
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
...s and the GLM model, thus showing an adequate measure for the different variables. The study notes the small sample size. This brings up an issue of external validity, and being able to generalize the results to a wider population outside of their college students (Cozby, 2009).
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
Quantitative methods in the social sciences are an effective tool for understanding patterns and variation in social data. They are the systematic, numeric collection and objective analysis of data that can be generalized to a larger population and seek to find cause in variance (Matthews and Ross 2010, p.141; Henn et al. 2009, p.134). These methods are often debated, but quantitative measurement is important to the social sciences because of the numeric evidence that can be used to drive more in depth qualitative research and to focus regional policy, to name a few (Johnston et al. 2014). Basic quantitative methods, such as descriptive and inferential statistics, are used regularly to identify and explain large social trends that can then
This article hasn’t provided an introduction; however a lengthy summary of the study which identifies the problem, purpose and rationale for the research study has been provided in the background. The introduction should give the reader a general sense of what the document is about, and preferably persuade the reader to continue reading. This prepares the reader for reading the rest of the document (Burns & Grove, 2001 p.636; Nieswiadomy, 2008 p.380; Stockhausen and Conrick, 2002).
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...
...the data did not involve member checking thus reducing its robustness and enable to exclude researcher’s bias. Although a constant comparative method was evident in the discussion which improved the plausibility of the final findings. Themes identified were well corroborated but not declared was anytime a point of theoretical saturation Thus, the published report was found to be particularly strong in the area of believability and dependability; less strong in the area of transferability; and is weak in the area of credibility and confirmability, although, editorial limitations can be a barrier in providing a detailed account (Craig & Smyth, 2007; Ryan, Coughlan, & Cronin, 2007).
This chapter covers the background of the study, statement of the problem, objectives, hypothesis or research questions, significance of the study, the scope and limitation, delimitation assumptions of the study and operational definitions.
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
In this paper, I will define quantitative and qualitative research methods and provide examples in the context of social issues which will hopefully provide insight into how this methods are properly applied.
Traditional research may use quantitative or qualitative research method. According to Hendricks (2009), quantitative research is a general conclusion based on hard data. Hen-dricks describe quantitativ...
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.