Several things can be done to the raw data in order to see what they can say about the hypotheses (Neuman, 2003). An inspection of the raw data can be done by using the descriptive statistics to find obvious coding errors. The minimum and maximum values for each variable must fall within the admissible range. Pairwise correlations depict that all relationships must be in the expected direction. Meanwhile, listwise deletion of missing values indicates that the data can be used for analysis.
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.
The section on Exploring Bivariate Data explains scatter plots in depth, discussing residuals, influential points and transformations, and other topics specific to scatter plots. Conditional relative frequencies and association, and marginal frequencies for two-way tables were explained in the section entitled Exploring Categorical Data. Overview of Methods of Data Collection explained the difference between censuses, surveys, experiments, and observational studies. Surveys are discussed more in depth in Planning and Conducting Surveys, including characteristics of a well-designed and well-conducted survey, and sources of bias. Planning and Conducting Experiments explains experiments in depth; going over confounding, control groups, placebo effects, and blinding, as well as randomization.
How the sample size is determined and the way participants are invited into the research is included within the write up. The data collected can be from a multitude of methods including interviews, questionnaires, attitude scales, or observational tools. When asking questions, the choices are typically closed-ended with fixed answers. The data analysis for quantitative studies includes complex language and statistical tests. The researcher identifies what statistical method was used and the results.
Describe the similarities and differences between correlation and regression. Correlation is a way of measuring the extent to which two variables are related. Correlation lets us know if there is a relationship or no relationship present between the two different variables. Regression on the other hand is a way of predicting the value of one variable from another. Regression is a hypothetical model of the relationship between the two variables.
These are commonly known as descriptive statistics. Nowadays, a major emphasis of the statistics is the evaluation of information present in data and the assessment of the new learning gained from this information. This is the area of inferential statistics and its associated methods are known as the methods of statistical inference. Descriptive statistics help summarize the sample. Procedures for statistical inference allow us to make generalizations about the population from the information in the sample.
It can also be used to summarize or describe any outfit whether it is a population or a sample, as in the preliminary stage of statistical inference the elements of a sample known. Statistical Inference refers to the process of making generalizations about the properties of the whole population, based on the specific, which shows their implicit a number of risks. To these generalizations are valid sample must be representative of the population and the quality of information should be controlled , as well as the conclusions and lessons I are subject to errors, you need to specify the risk or probability that one can commit those mistakes. Inferential statistics is the set of techniques used to draw conclusions that go beyond the limits of the knowledge provided by the data, looking for information of a collective through a methodical process of managing sample data. DESCRIPTIVE STATISTICS: It is the branch of statistics that deals with the collection, presentation, description, analysis and interpretation of a dataset.
Numerical summaries which can either measure the central tendency of a given set of data or which describe the spread of a given data. They use ... ... middle of paper ... ...he data. It will be also paramount to investigate the results using evaluative means like the ANOVA test, which uses variance and makes sure that averages exists within every variable test group. There is also need to set a regression, which is a general statistical tool, which sees how variables are interconnected. Finally, there is the need to analyze the qualitative element of the analysis (Chance et al, 2005).
This theory represents causal processes that generate observations on multiple variables. (Yuan & Bentler, 1998) The SEM procedure starts with model specification that links the variables assumed to affect other variables and directionalities of their effects. (Kline, 2011) Specification is a way of structural relations being modelled pictorially to enable clearer conceptualisation of the theory under study. In the estimation process, SEM produces regression weights, variances, covariances and correlations in its iterative procedures converged on a set of parameter estimates. (Holmes-Smith, Coote, & Cunningham, 2004) On specification the model is then tested for plausibility based on the sample data that comprise all observed variables in the model.
It is a statistical tool that permits the researchers to investigate how multiple independent variables are related to a dependent variable (Higgins, 2005) (D. Allison, 1999). Besides, multiple regression will also combine multiple variables to generate dependent variable’s optimal predictions (D. Allison,