This paper is an illustration of quantitative data analysis using the IBM SPSS Statistics software. It does not provide the details of technical skill to operate SPSS but focuses on developing a set of decisions and actions in order to set up, describe, manipulate and analyse data in the specific context of the study of Jackson and Mullarkey (2000). In order to fulfil the task, this paper illustrates a step-by-step of actions that were made on the data. It also gives the insight into the determination of each step that helps interpret the findings from the data.
1. DATA SET UP IN SPSS
It is important to set up the data before conducting further activities on data by using SPSS. The establishment of data needs a preliminary handling of the raw data in Excel and then defines the data characteristics and deals with missing variables in SPSS.
(1a) Prepare Excel file
Review the raw data file in Excel
Additional coding: Replace the text into numbers o Column Site of Location: Replace A with 1, B with 2, C with 3, D with 4 o Column Gender: Replace Female with 1, Male with 2 o Column Type of Work Design:
Replace PBS Work Design with 1,
QRM Work Design with 2
and then replace Work Design with a blank space (considered Work Design as a missing value because it did not reflect the choice between PBS Design and QRM Work Design)
(1b) Import the Excel file into SPSS
Save recent changes
Close Excel before open data from SPSS
(1c) Define the variables: Make changes in the Variable View
Name: Change the labels adapted in the first row of Excel file into new variable names (regard the variables background of the conceptual framework, must be short, no space), as indicated in the below table.
Type of variables: Numeric
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... middle of paper ...
...o the Interval and Ratio variables of the Numerical data. Furthermore, the Categorical data are often accompanied by non-parametric statistics; the Numerical data are often used with parametric statistics. In short, the measurement of data (Numerical vs. Categorical) and the kind of statistics (parametric vs. non-parametric) will distinguish one statistic from another.
Be able to interpret the statistical results: A significant step in analysing data is the explanation of the statistics. SPSS dedicates to create the statistical results very quickly but it is the responsibility of the analyst to understand and to express the finding from the software logically. It is the critical point to determine and demonstrate the exploration of the research. Such a misunderstanding or a wrong interpretation of the statistical results can destroy the whole work of the research.
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.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
1. You want to insert a cell into your worksheet. Which command do you use?
12). These are the most common methods that are being used. The difference between qualitative and quantitative methods concerns how the data are collected, where basically qualitative data focus on words while quantitative focus on numbers (Denscombe, 1998, p. 173-174).
Descriptive statistics is a procedure of organizing sample data. This procedure allows readers to be able to understand and describe the data’s importance. Descriptive statistics allows an individual to quickly understand the data and make predict an individual score; however, descriptive statistics does not describe all data in the sample. Inferential statistics is a process that determines whether sample data accurately represented the relationship to the population. In other words, one uses inferential statistics to determine if the sample data is believable.
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).
The articles, published after 1996, contain varied methods of research attainment, but share similarities such as being a self-survey, having a small sample size, and being
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).
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...
Quantitative research uses a deductive reasoning also known as top to bottom or (top down approach) starting with a theory, then the hypothesis, followed by observation and finally confirmation , going from the general to the more specific. Quantitative methods use numbers and statistics to show the results of the research exercise and mainly are concerned with mathematics and statistics. In quantitative research there are levels of measurement being firstly nominal which are names of things followed by ordinal sequence of things, interval where the sequence has equal distance between each item, and ratio where there is a true zero (Alston & Bowles, 2003, p. 7-9).
The nature of research instruments, the sampling plan and the type of data the research design constitutes the blueprint for the collection, the measurement and analysis of data. It aids the researcher in the allocation of his limited resources by posing crucial choices.
To do this I needed to use the software Microsoft Excel 2003 as it was easier to use than other software products. As well Microsoft Excel can perform more useful functions such as Absolute Cell Reference, Functions (MIN, MAX and AVERAGE),Conditional Formatting and many more. Each of these useful in a case such as this.
4. Determining the Sample Size: Determining the sample size involves several qualitative and quantitative considerations, such as the importance of the decision; the nature of the research; the number of variables in...
Quantitative studies are primarily numbers based. They deal with large cohort groups as well as analyze large amounts of data. “A quantitative researcher typically tries to measure variables in some way, perhaps by using commonly accepted measures of the physical world (e.g., rulers, thermometers, oscilloscopes) or carefully designed measures of psychological characteristics or behaviors (e.g., tests, questionnaires, rating scales)” (Leedy & Ormrod, 2010, p. 94).
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...