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# Statistical Methods for Psychological Research - II

CEC via Swayam

### Overview

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

### Syllabus

Week 1Overview on Inferential StatisticsHypothesis Testing with Small Sample Sizes (t test)Hypothesis Testing with Large Sample SizesAssumptions in Hypothesis Testing for a Single Mean; the Null and the Alternative Hypotheses
Week 2Choice of Ha One-Tailed and Two-Tailed Tests; Steps for Hypothesis TestingHypothesis Testing about a Single Mean and CalculationThe Statistical Decision Making Regarding Retention and Rejection of Null HypothesisEstimating the Standard Error of the Mean when σ is Unknown
Week 3t Sampling Distribution - Description, Characteristics, Computing with Definitional FormulaOverview of Statistical Inference and Hypothesis TestingCritical Value, Significance Level and p-valueA Statistically Significant Difference versus a Practically Important Difference
Week 4Errors in Hypothesis Testing and Power of a TestHypothesis Testing about the Difference between Two Independent Mean - An OverviewNull and Alternative Hypotheses; the Random Sampling Distribution of the Difference between Two Sample Means
Week 5Properties of Sampling Distribution of the Difference Between Two Sample MeansDetermining a Formula for t-distributionTesting the Hypothesis of No Difference between Two Independent Means; Use of One-Tailed Test
Week 6Assumptions Associated with Inference about the Difference between Two Independent MeansHypothesis Testing about the Difference Between Two Dependent (Correlated) Means – An OverviewThe Null and Alternative Hypotheses for Testing the Difference between Two Dependent (Correlated) Means
Week 7Determining a Formula for t in Testing the Differences Between Two Dependent (Correlated) MeansDetermining t test statistic, Properties and Degrees of Freedom for Tests of No Difference Between Dependent MeansTesting a Hypothesis about Two Dependent Means using the Formula Involving Standard Errors and Correlation OnlyAssumptions when Testing a Hypothesis about the Difference between Two Dependent Means
Week 8Null Hypothesis and Alternative HypothesisHypothesis Testing for Differences among Three or more GroupsBasis, Assumptions within and between - Group Variances of One-Way ANOVA
Week 9Partition of Sum of Squares and One-Way ANOVA ProcedureApplication of One-Way ANOVAPost-Hoc Comparisons in One-Way ANOVARaw Score Formula and Comparison of t and F Test
Week 10Hypothesis Testing for Categorical Variables and Inference about FrequenciesThe Chi-Square as a Measure of Discrepancy between Expected and Observed FrequenciesLogic of the Chi-Square Test; Assumptions of Chi-Square
Week 11Calculation of the Chi-Square Goodness of Fit Test One-Way ClassificationChi-Square for Two-Way Classification Variables - Contingency Table AnalysisInterpretation of the Outcome of a Chi-Square Test
Week 12Non-Parametric Approach To Data AnalysisNon-Parametric Test for One Sample and Two Independent SamplesNon-Parametric Test for Two Dependent Samples
Week 13Non-Parametric Tests for More Than Two SamplesIntroduction to SPSSStructure of SPSS
Week 14Getting Started with SPSSFormatting of Excel Data PlatformExporting Excel To SPSS
Week 15Formatting of SPSS PlatformExploring Descriptive Statistics in SPSSExploring Different Statistical Graphs and Tables

### Taught by

Dr. Tirupathi Rao Padi

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