Psychological Research Methods PDF

PSYCHOLOGICAL RESEARCH Nicole Marshall, MS, MHC, MOT Psychology Teacher Manchester regional High School Objective  Introduction to Scientific Method  Intro to conducting Psychological Research  Exploring Ethical principles of research Importance of Research Historical: Every aspect of hum...

PSYCHOLOGICAL RESEARCH Nicole Marshall, MS, MHC, MOT Psychology Teacher Manchester regional High School Objective  Introduction to Scientific Method  Intro to conducting Psychological Research  Exploring Ethical principles of research Importance of Research Historical: Every aspect of human living and life is based on research and exploration. Beliefs were created out of misconceptions. Without supported evidence an idea can not be validated as true or untrue. Validating claims: Research plays a critical role in validating claims and ensuring they are based on evidence. Without research, our understanding of the world would be filled with inaccuracies and falsehoods. Empirical Nature of Scientific Research:: Scientific research is empirical, meaning it relies on objective, tangible evidence that can be repeatedly observed and verified, regardless of the observer. This empirical approach ensures that findings are reliable and not subject to individual biases. What is Scientific Research  A method of learning about the world through the application of critical thinking and tools such as observation, experimentation, and statistical analysis. Observation Observation  The scientific process begins with observations of the natural world or a specific phenomenon.  Researchers identify a question or problem to be addressed based on these observations.  Inductive and Deductive observations assist in creating a theory.  A Theory is a well-established explanation for a phenomenon, based on a set of interconnected concepts and propositions, that aims to explain, predict, and understand observed patterns or behaviors within a specific domain, often supported by evidence from multiple studies and considered to be generally accepted within the field;  essentially, it's a comprehensive framework for interpreting and making sense of data.  A theory is a well-developed set of ideas that explains observed phenomena.  Scientists use inductive reasoning to develop theories. The Process of Scientific Research: Inductive Reasoning:  Deductive Reasoning: Involves drawing a general  Involves making specific predictions based on a conclusion from specific general principle or premise. observations. Example: Observing that many fruits  Example: All living things require energy to grow on trees and concluding that all survive (general premise). Ducks are living fruits grow on trees. things; therefore, ducks require energy to survive (specific conclusion) Scientific Method Application Scientists use deductive reasoning to formulate theories and hypotheses. These hypotheses are tested through empirical observations. Inductive reasoning is used to form conclusions from these observations. These conclusions can lead to new theories, hypotheses, or broader generalizations. Research Process  Prediction: from the hypothesis, researchers make a specific prediction about the outcomes of an experiment or observation.  These predictions are formulated in a way that allows them to be tested empirically. Hypothesis Formation A hypothesis is a tentative and testable prediction or explanation based on observations and existing knowledge between one or more variable and serves as the basis for further investigation. A good theory must have a testable prediction or hypothesis. It predicts how the world will behave if the theory is correct. Hypotheses are often structured as "if-then" statements and must be falsifiable, meaning they can be proven incorrect. One must think about possible biases in the observations. The research must allow others to replicate or repeat the experiment Hypothesis  All hypotheses must be able to be falsified.  Falsifiability is the possibility that an assertion/prediction can be shown false by an experiment or observation.  It means that if it is false, some observations or experiment will produce a reproducible result that is in conflict with it. Experimentation  Researchers design and conduct experiments to test the predictions derived from the hypothesis.  Experiments involve manipulating one or more variables (independent variables) while keeping other factors constant and measuring the effect on another variable (dependent variable).  Variables are elements that are identified in the hypothesis or research question. There are two types of variables. Variables  Independent Variable Definition - The variable that the experimenter manipulates or controls.  Role in Experiments ⚬ This is the main difference introduced between the experimental and control groups. ⚬ It is the factor that is expected to cause an effect or change in the dependent variable.  Dependent Variable Definition - The variable that is measured by the researcher. Role in Experiments ⚬ It shows the effect or outcome of changes made to the independent variable. ⚬ The researcher examines this variable to determine how much influence the independent variable has had. Variables  A good way to remember which is which:  Independent Variable (IV) = stimulus or cause  Dependent Variable (DV) = response or effect  One goal when conducting experiments is to make sure it is valid.  Validity refers to the extent to which a test measures what it is supposed to be measuring. Data Collection and Analysis  During the experiment, researchers collect data to assess the outcomes and test the predictions.  Data analysis involves statistical techniques to evaluate the results and determine whether they support or reject the hypothesis. Types of Data Collection Primary Data Collection: Gathering data directly from participants through methods like surveys, interviews, or observations. Considered more reliable as researchers control data collection process. Secondary Data Collection: Utilizing data that has already been collected by others, like government reports, published studies, or organizational records. Often faster and cheaper to access than primary data. Qualitative Data Collection: Non-numerical data that focuses on understanding meanings, experiences, and perspectives. Qualitative Data Collection: Numerical data that can be statistically analyzed to identify patterns and trends. Data Collection Methods Surveys: Structured questionnaires used to gather information from a large sample of respondents. Interviews: Direct conversations with individuals to collect detailed insights and opinions. Observations: Watching and recording behaviors or events in a natural setting. Focus groups: Facilitated discussions with a small group of people to explore opinions and perspectives. Document analysis: Examining existing documents to extract relevant information. Social media monitoring: Tracking online conversations and interactions on social media platforms. Analyzing the Results  During the experiment, researchers collect data to assess the outcomes and test the predictions.  Data analysis involves statistical techniques to evaluate the results and determine whether they support or reject the hypothesis.  Analyzing the results of a research project involves examining the collected data to interpret its meaning, drawing conclusions based on the findings, and assessing how they align with the original research questions and hypotheses, considering factors like statistical significance, limitations, and potential biases within the study design. Key Steps to Analyzing Data  Data Cleaning and Organization: Identifying and addressing any errors or inconsistencies in the data. Organizing data into a manageable format, such as spreadsheets or statistical software.  Descriptive Statistics: Calculating measures of central tendency (mean, median, mode) and dispersion (standard deviation, range) to summarize key characteristics of the data  Inferential Statistics: Applying statistical tests to determine whether observed relationships between variables are statistically significant, allowing for generalizations to a larger population  Inferential Statistics: Applying statistical tests to determine whether observed relationships between variables are statistically significant, allowing for generalizations to a larger population Interpretation of Results Explaining the meaning of the results in the context of the research question and existing literature. Identifying patterns, trends, and relationships within the data Based on the data analysis, researchers draw conclusions regarding the hypothesis. If the results support the hypothesis, it may be considered provisionally accepted. If the results do not support the hypothesis, researchers may revise or reject it and formulate new hypotheses for further investigation. Important Considerations when Analyzing Research results Evaluate how the study design (e.g., Research Design: experimental, correlational) affects the interpretation of results. Sample Assess whether the study sample accurately reflects the target Representativeness: population. Identify potential sources of bias in data collection, analysis, and interpretation Bias: and discuss their impact on the findings. 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