Statistical Methods for Psychological Research - II

The concept of statistical inference is the most popular mechanism in exploring the interpretations on several research hypothesis. Meaningful conclusions on the study of hypotheses can be arrived through these contents. The intended objective is to address the part-2 of statistical inference, i.e. Testing of Statistical hypotheses more suited with research in psychological studies. Hence, this course has the significance scope on usage of different statistical techniques for psychological research methodology.This course is designed by keeping in mind about the students of Graduate (Hons) of the universities and other HEIs. It has given focus on the parametric as well as non-parametric testing of hypotheses. Further, the syllabus contents are designed to deal with the tests of the varying sample sizes namely small sample tests, exact sample tests and large sample tests. The inference methods on the relational measures like correlation between variables and association between the attributes are included. As the current trends are with the machine assisting statistical computing, the basic required data handling procedures are discussed through SPSS. Different aspects such as handling of data processing begin with from the data collection to the report making are also covered.Inference procedures that consists of (i) defining research objectives, (ii) formulating the null and alternative hypothesis, (iii) defining the formula for the test statistic, (iv) computing the test statistic for the data under illustration, (v) finding the test critical value for the given level of significance, required degrees of freedom and the type of the test (one tailed/ two tailed), (vi) drawing the conclusions on the objectives of the test, etc. are discussed elaborately. The procedures through chi-square test, t- test, F test and Z- test, etc. for handling the tests of proportions, Means, variances, correlation coefficients, etc. are explained in detail by adopting the conceptual understanding through numerical examples.About 50 modules are designed with due weightage on the contents of diversified categories like (i) Familiarity on the terminology, (ii) theory behind test procedures, (iii) understanding the test mechanism through illustrations, (iv) computing through usual statistical methodology by means of using scientific calculators, (v) statistical computation through software such as MS Excel, SPSS, etc. This course material is prepared with the student’s perception in mind by following straight and simple explanations, learner friendly approaches, practicing with numerical examples, etc. Hope the students of non-mathematical orientation will enjoy the course contents by getting in out of the essential concepts.Course Objectives:After completion of this course, the students are enable with good concepts of statistics for dealing the statistical inference, testing the statistical hypotheses phase with all the effective lines. This online course is having the objectives of1. To provide the essential knowledge of statistical science for dealing the research methodology in Psychology.2. Statistical Inference is the main focus with more emphasis on the testing of statistical hypothesis3. To impart the knowledge on the handling procedures of Both parametric and non-parametric tests in the contexts of Small and large sample size cases.4. To provide the theoretical and conceptual understanding of different statistical tests with illustrations5. To train the students through problem solving and enable them for having proper understanding of statistical methods for psychological research.Course Outcome:The course entitled “Statistical Methods for Psychological Research -2” is designed with the view of extending the following outcome.1. Overview and detailed descriptions on different terminology and glossary of the Testing of hypothesis2. Detailed understanding on the Null and the Alternative Hypotheses Research Hypothesis3. Test procedures with on Small and Large sample tests namely t-test, F-test, Chi-square Test and Z-test4. Detailed understanding of Parametric and Non-Parametric Testing of statistical Hypotheses5. Numerical examples on tests for Proportions, Means, Variances, Correlation Coefficients, Good Ness of Fit, Independence of Attributes, ANOVA6. Statistical computing with MS Excel and SPSS