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The importance of data analysis in qualitative research
Qualitative and quantitative research methods
Qualitative and quantitative research methods
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3.5 Data analysis methods
According to Pallant (2007), the obtained data needs to be analysed and interpreted very carefully in order for the researcher to gain valuable and useful information from the study (p. 100). The researcher should have at least understanding statistical techniques used in the study for data analysis. Moreover, Hair et al. (2003) have also mentioned that it is very important to determine the goodness of the collected data and analyse it accurately because the wrong data will lead the useless result (p. 306).
In this study, the quantitative analysis methods will be adopted for the data analysis procedure, the obtained data from the stage of questionnaire pretesting and the final survey will be analysed by the difference
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Basically, there are two factor types of factor analysis which comprise of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) (DeCoster 1998, p. 1). Exploratory factor analysis (EFA) is generally applied in the early stage of the research in order to collect the data regarding the interrelationship among set of variable. While, confirmatory factor analysis (CFA) is more complex which is used to confirming specific theories or hypotheses concerning the structure underlying a set of variables (Pallant 2007, p. 179).
For this study, exploratory factor analysis was used to explore the interrelationship among the variables and to reduce data in the stage of questionnaire pretesting. To analyse data by applying factor analysis, Kaiser-Meyer-Olkin (KMO) need to be measure in order to identify the sampling adequacy. Also, the appropriateness of factor analysis can be determined by considering KMO and Bartlett’s test of sphericity (Hair et al., 2006). Hence, analyzing the obtained data from the process of questionnaire pretesting will be able to determine problems with the questionnaire before carrying out the final
Evaluate the appropriateness and thoroughness of the data analysis procedures, and clarity of the results presentation. Do not become overly concerned about technical statistical aspects of the analysis.
Three major types of methods used for this study are “Longitudinal Research Method”, “Cross- sectional Research Method” and “Cross Sequential Method” (A cohort form of Longitudinal and cross-sectional method). “Case Study Method” and “Survey Method” also have been used (Baltes, 1968).
Head to head hits are still a prevalent issue in the Nfl. Should head to head hits lead to an ejection from the game? If a player in college football commits an intentional helmet to helmet hit, the penalty is an ejection. If college football can input this penalty, couldn’t the NFL? We see that former football players who suffered many concussions over their playing careers, have long lasting effects. Some current players feel as though that they can’t control where their hits land and injuries are just a part of the game. Some fans feel as though all these penalties are taking the fun away the game. Former NFL players are an example of what helmet to helmet hits can do.
Evaluating and understanding research findings is a very important skill for professionals to acquire. It is necessary to thoroughly collect data, findings, and results of the experiments to produce accurate detailed accounts of the studies.
...to ensure results are a true representation of participant opinion. The researcher to share a clear account of the methods, data collection and analysis used in the study.
The authors of this article have outlined the purpose, aims, and objectives of the study. It also provides the methods used which is quantitative approach to collect the data, the results, conclusion of the study. It is important that the author should present the essential components of the study in the abstract because the abstract may be the only section that is read by readers to decide if the study is useful or not or to continue reading (Coughlan, Cronin, and Ryan, 2007; Ingham-Broomfield, 2008 p.104; Stockhausen and Conrick, 2002; Nieswiadomy, 2008 p.380).
Thematic analysis is espoused to be the foundational approach to qualitative analysis and methods (Saunders et al., 2016 as stated in Braun and Clarke, 2006: 78) and it is a useful method used to identify and analyse the order and patterns of qualitative data (Attride-Stirling, 2001). Qualitative research method depicts the correlation that exists between data and events, creating the pictorial representation of what one thinks a given data says (Saunders et al., 2016). They also opined that, qualitative data analysis is cogent, interactive and iterative. Also, Joana and Jill (2011) and Saunders et al (2016) postulate that, qualitative research brings meanings from words and images as opposed to numbers. However, despite its robustness and rigour of its application, it is skewed more to the interpretivist ideologies since researchers draw conclusion from participants and the hypothesis being forecasted (Joana and Jill, 2011; Saunders et al., 2016).
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...
Factor analysis studies where conducted and the results where used in an analysis technique generally done with computers to determine meaningful relationships and patterns in behavioral data. Beginning with a large number of behavioral variables, the computer finds relationships or natural connections where variables are maximally correlated with one another and minimally correlated with other variables, and then it groups the data accordingly. After this process has been repeated many times a pattern of relationships or certain factors that capture the essence of all the data appears (Pervin & John 1999). The same process used to determine the Big Five Personality factors; copious amounts of different researchers that have done numerous tests and they all agree that the “Big five Factors” are the only consistently reliable factors that have been found.
The two questions were designed to provide useful information. The respondents who are female and age between 18-24 or 25-35 contributed to the research. Others were seen as invalid questionnaires. The third section is the most important section of the questionnaire. There were ten closed questions in the third section which follow an easy to hard order, but eight of them were single answer questions whereas the rest two were multiple choice questions.
Moreover, the data can be used for various types of research and many findings and outcomes can be analysed to create new theories or studies (Levin, 2006; Detels, et al., 2011; Setia,
The primary factors that are important in conducting statistical test are variables (categorical or quantitative) and the number of (IVs) independent variables and (DVs) dependant variables. To facilitate the identification process the chapter provides two decision- making tools so that it is easier to make a decision. The chapter presents the decision making tools and gives an overview of the statistical techniques addressed in this text as well as basic univariate test, all of which will be organized by the four types of research questions: degree of relationship, significance of group differences, prediction of group membership, and structure. Statistical test that analyze the degree of relationship include bivariate correlation and regression, multiple regression and path analysis. Research questions addressing degree of relationship all have quantitative variables. Methods that examine the significance of group differences are t test, one-way and factorial ANOVA, one-way and factorial ANCOVA, one- way and factorial MANOVA, and one-way and factorial MANCOVA. Research questions that address group differences have categorical IVs. Statistical tests that predict group membership are discriminate analysis and logistic regression. Research questions that address prediction of group membership have a categorical DV. Statistical
Assist students in appreciating the need for taking precautions in collecting, analysing and interpreting statistical data.
The selection of the survey as the most appropriate research method can come about due to the need for the investigation to reflect on qualitative data from the participants to assist in drawing practical conclusions. The researcher believed in the need for selecting a research method that would be of value in projecting the overall expectation of the study, which is to examine a relationship between the
Due to the nature of the study, a number of limitations and delimitations are imposed upon the research, so the following points should be taken in to account: