Uni 1- Intro to Stats (PSYC1010) PDF
Document Details
Uploaded by InvigoratingAgate5075
University of Southampton
Tags
Related
- BEHL 2005/2019 (UO) Introductory Research Methods PDF
- Psychological Statistics BPSY 55 PDF
- PSYC 204 Introduction to Psychological Statistics - Week 1 Lecture Notes (2024-09-03) PDF
- Psychological Statistics PDF
- PSY201: Introduction to Quantitative Research in Psychology Lecture Notes PDF
- PSY 201 Introduction to Statistics for Psychology I Lecture Slides PDF
Summary
This document provides an overview of a university course, specifically PSYC1010 Introduction to Statistics, including topics on research methodology, the scientific method, and psychological research challenges. Key topics covered include challenging areas in psychological study and research methodologies, and the types of logical inferences and causal influences. The document also touches on basic and applied research.
Full Transcript
Midterm (21st Nov)= 29.5%.... Design a study 4 MCQ= 8% Final= 60% Research participation= 2.5% Module content found in my courses psyc1010 Core book= Andy Field’s “discovering stats using IBM SPSS Statistics” Other books= Howitt and Cramer’s “ intro to research methods in psychology” Miles and Ban...
Midterm (21st Nov)= 29.5%.... Design a study 4 MCQ= 8% Final= 60% Research participation= 2.5% Module content found in my courses psyc1010 Core book= Andy Field’s “discovering stats using IBM SPSS Statistics” Other books= Howitt and Cramer’s “ intro to research methods in psychology” Miles and Banyard’s “ understanding and using stats in psychology” ALL AVAILABLE IN LIBRARY PADLET for general queries ______________________________________________________________ Methodology- the study of methods Scientific method- aims to protect us from our biases, limitations and interests Challenges in psychological research= - 1) unobservable object of investigation (e.g. thoughts and feelings) - 2) subjectivity of ‘object of investigation’ (biases) - 3) social construction (agreed rules or smth of society) - 4) ethics (negative consequences of our investigations (on society and the person)) Focused more on in week 4 and 5 Stats are used to prove a certain effect Data analysis to prove effect or lack of Research= Basic research: 1) To increase the stock of knowledge Applied research: 2) To increase the use of knowledge to devise new applications Belief (subjectively true) —> Check for Proof —-> Knowledge (objectively true) Science- building knowledge using research methods Scientific research can always be checked… dependant on the proofs not the person Science is democratic as everybody has the right to interrogate, criticise, doubt and verify Need procedure in scientific research to verify (reference scientific sources) and share (communication and collaboration) knowledge Hypotheses- are unproven, provisional statement Conclusions- proven Logical proof- does it make sense?- Philosophical origin in rationalism This with logical arguments and maths formulation you can create theories Empirical proof- is there evidence?- Philosophical origin in empiricism This gives data 3 types of logical inference- 1) Deduction= infer a proposition from a general statement (only one that gives a necessarily true result) 2) Induction= infer a general statement from statements about single cases (may give a true result) 3) Abduction= Infer from cases to the most likely, best explanation (may give a true result… but is misleading) 3 types of causal influence- 1) Necessary cause- The cause is necessary to produce the effect ( but might not be sufficient) 2) Sufficient cause- the cause alone produces the effect 3) Contributory cause- the cause contributes to an effect by increasing the likelihood or the strength Scope- general validity of propositions Fruitfulness (how can your findings help answer others questions)- implications beyond research questions A proposition is novel if it is surprising and informative. Very novel findings are ground-breaking (open up new paths of research) Simpler the theory the better as it makes less assumptions (Parsimony) Propositions with less assumptions are better because each assumption risks being wrong. Occam’s razor- cut out unnecessary assumptions Conservatism- integration in existing knowledge (makes things more plausible) Minimise new assumptions that contradict existing knowledge. Propositions that integrate with existing knowledge are more likely to be true. After an empirical test: - Data analysis - Conclusion - Implications