Univariate analysis is the simplest form of quantitative (statistical) analysis. Univariate analysis explores each variable in a data set, separately. It looks at the range of values, as well as the central tendency of the values. It describes the pattern of response to the variable and also describes each variable on its own. Univariate analysis was performed so as to facilitate more complicated analyses, like bivariate and multivariate analysis. Univariate descriptive statistics describe individual variables. In this section we were trying to describe the descriptive statistics which summarize the data. We use pie chart, bar diagram, doughnut chart, column diagram for graphical representation for explaining the descriptive statistics. We …show more content…
Association is based on how two variables simultaneously change together; the notion of co-variation. Bivariate descriptive statistics involves simultaneously analyzing (comparing) two variables to determine if there is a relationship between the variables. The purpose of this chapter is to go beyond Univariate statistics, in which the analysis focuses on one variable at a time.
To do this analysis we used cross tabulation technique for finding association among variables. Initially we test that two variables are associated or not. If two variables are associated then we find strength of this association by appropriate statistic. Cross tabulations can be produced by a range of statistical packages, including some that are specialized for the task. By using the cross tabulation technique, in this section we will analyze the association between variables. We will also test the correlation among them. Here we will explain the summary of the results.
Bar Plot
A bar chart or bar graph is a chart that presents grouped data with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column bar
A person should be able to describe the monthly costs to operate a business, or talk about a marathon pace a runner ran to break a world record, graphs on a coordinate plane enable people to see the data. Graphs relay information about data in a visual way. If a person read almost any newspaper, especially in the business section, they will probably encounter graphs.
6. Why is it important to use both graphs and summary indices (e.g., the means) to interpret the results of a study—or is it?
Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable;[2] for example, correlation does not imply
In statistics, a population is a collection of individuals, things, events, etc. The population is the topic that one wants to make inferences on, whereas a sample is a subset of the population that is being collected—to be studied. After the sample is studied in statistics, one draws an inference of the population. There are four general sampling methods used in statistics: representative sample, random sample and quasi-random sample, stratified and quota sample, convenience sample, and purposive sample. A representative sample should be unbiased and thus properly indicate a characteristic of the entire population. In a random sample nothing is biased; in other words, every individual, thing or event in the population has the same chance of being selected for the sample. Therefore, because of the randomness of the sampling, the selection of one item from the population in no way effects the selection of another item. A quasi-random sample is simply a number (nth), which is
In response to the question set, I will go into detail of the study, consisting of the background, main hypotheses, as well the aims, procedure and results gathered from the study; explaining the four research methods chosen to investigate, furthering into the three methods actually tested.
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.
Trochim, William M.K. "Descriptive Statistics." Descriptive Statistics. Web Center for Social Reseach Methods, 20 Oct. 2006. Web. 28 Apr. 2014. .
With SPSS, one was able to determine which variables got attached to which existing factors and created a strong association between them. This assisted in finding out why certain variables are drawn towards one factor over another (Field, 2013). The data was easy to manage using the SPSS, the results were easily displayed, analyzed and cross-references (Costa & McCrae, 1995).
Nvivo 10 software was used in analyzing the data by methodologically coding and categorizing the data in open, axial and selective coding. Five major themes emerged from the
A correlational method measure relationship between two or more variables: independent variable(s) and dependent variable. The independent variables are the experimental factors that the researcher can manipulate, while dependent variables are the things that the experimenter no control over, that include the outcome of the experiment (Class notes). The experimental method explores cause and effect of the study (David G. Myers, 2008).
Liu, C., Spector, P. E., & Shi, L. (2008). Use of Both Qualitative and Quantitative
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...
Two of the most useful types of statistics are known as descriptive and inferential statistics. Descriptive statistics is the term that is used to describe the analysis done to summarize the data from a population in a meaningful way; typically, through graphs and charts. On the other hand, inferential statistics is a way of making generalizations about a population of interest from a small sample size (Descriptive and Inferential Statistics, n.d.).
The analysis of data usually begins with such descriptive statistics as the mean, median, mode, variance and standard deviation. Mean, median, and mode are known as measurements of the central tendency of the data and range, standard deviation, variance give the information about the variability of the data. The mode should be preferably used when calculating measure of center for the