Goals of Scientific Enterprise in Psychology

Goals of Scientific Enterprise in Psychology

• Measurement and Description initially quantifies behavior through systematic observation and data collection
• Psychologists aim to provide precise descriptions to capture the essence of what is studied
• Understanding and Prediction develops causal explanations for behaviors and makes predictions based on variable relationships
• Understanding identifies mechanisms driving behavior
• Prediction foresees outcomes based on known characteristics
• Application and Control uses psychological knowledge to positively influence behaviors and improve outcomes
• It involves applying research findings in practical settings
• It is essential for translating findings into real-world benefits

Hypothesis, Variable, and Theory

• A hypothesis is a specific, testable prediction about the relationship between two or more variables
• An example would be that “Increasing exercise will reduce levels of anxiety”
• A variable is any factor that can be manipulated or measured in a study
• The independent variable is the factor manipulated
• The dependent variable is the outcome measured to see if the independent variable affects it
• A theory is a broader conceptual framework synthesizing findings to explain a general phenomenon
• Cognitive-behavioral theory explains how cognitive processes influence behavior
• Hypotheses are narrow, with specific predictions, while theories are comprehensive frameworks

Testability of Scientific Theory

• A scientific theory must be testable to be confirmed or falsified through empirical evidence
• "Testable" means it can be subjected to observation, experimentation, or data collection to assess validity
• Testability enables researchers to design studies to support or refute the theory based on systematic evidence
• Without testability, a theory is speculative with no scientific credibility

Steps in Scientific Investigations

• The first step is to formulate a testable hypothesis
• It involves creating a specific prediction that can be measured through research
• An example would be that students who engage in regular physical activity will exhibit lower stress levels during exams
• The second step is to design the study outlining a research plan and methodology for testing the hypothesis
• It includes deciding on participant selection and data collection techniques
• Appropriate controls and measurements are essential during this phase
• The third step is to collect data using experiments, surveys, or observational studies
• It follows the design established in the previous step
• The accumulation of data is crucial for later analysis
• The fourth step is to analyze the data using statistical methods to interpret the results
• This determines whether findings support or contradict the initial hypothesis
• It reveals patterns or insights within the data
• The final step is to report the findings through publications or presentations
• It includes a discussion of the implications of the findings and how they relate to existing knowledge and theories

Operational Definition in Research

• An operational definition involves the criteria or procedures used to measure or manipulate a variable in a study
• It enhances precision and clarity in scientific communication
• It is important in psychology due to the potential for vague meanings in everyday language
• Intelligence can be operationally defined as the score from a standardized IQ test
• Learning methods such as "massed practice" can be defined as three continuous hours of study.
• Anxiety can be operationally defined through physiological measures like heart rate or questionnaires
• Rigorous definitions are essential for replicability and comparability across studies

Research Participant

• A research participant is someone taking part in a study, contributing valuable research data
• Terminology has shifted from "subject" to "participant" to reflect active role
• The term "subject" is still acceptable for animal research

Data Collection Methods

• Direct observation involves trained observers recording behavior objectively, sometimes aided by tools/instruments
• The questionnaire involves participants answering written questions about attitudes, opinions, or specific behaviors
• Interviews involve face-to-face dialogue to obtain information about a subject’s behavior
• Psychological tests use standardized measures to sample behavior, usually assessing mental abilities or personality traits
• Physiological/neural recording monitors/records physiological processes like blood pressure or brain activity
• Examination of archival records analyzes existing institutional records like census, economic, or medical data

Scientific Journals and Peer Review

• Peer review involves a systematic evaluation of submitted articles before publication in a scientific journal
• The aim of peer review is to ensure the accuracy, quality, and credibility of research
• It typically involves a double-blind review where author and reviewer identities are concealed to promote unbiased feedback
• In a broader context, peer review also includes the continued assessment of research ideas post-publication by the scientific community
• Ongoing evaluation contributes to the collaborative nature of science, refining knowledge over time

Advantages of the Scientific Approach to Studying Behavior

• Clarity and Precision involves clarity and precision in definitions and measurements allowing effective communication and enhancing reliability
• Avoidance of Errors employs empirical support and skepticism to prevent acceptance of errors and unsupported relationships

Anecdotal Evidence

• Anecdotal evidence is personal stories used to support a claim or argument
• It is often based on personal experiences
• It should not be used because of:
• Limited samples, often from a single case
• Bias and Subjectivity because personal experiences are heavily influenced by bias and emotion
• Lack of Systematic Evidence due to the unsystematic gathering of data

Descriptive Research

• Observational Studies observe subjects in their natural environment without manipulating variables
• Strengths include providing rich qualitative data and real-world insights
• Weaknesses include limited control over extraneous variables leading to biased outcomes
• Case Studies examine a single individual or group
• Strengths include detailed insights and understanding of unique circumstances
• Weaknesses include findings not being generalizable to larger populations
• Surveys gather data from a sample group through questionnaires or interviews
• Strengths include efficient data collection from large diverse groups, allowing statistical analysis
• Weaknesses include that responses being influenced by question wording and the risk of sampling bias and respondent honesty

Descriptive/Correlational Research

• Descriptive Power allows descriptions of patterns and relationships between variables
• Ethical Flexibility is useful when experimental designs are unethical
• Causation Limitations are its primary disadvantage, as it cannot establish the cause-and-effect relationship

Experiments vs Observational Studies

• An experiment actively manipulated independent variables to observe their effect on a dependent variable
• It allows for causal inferences
• An observational study observes behaviors without manipulation
• It makes it difficult to establish cause-and-effect relationships
• Experiments have controlled conditions
• Observational studies capture data as it occurs naturally, sacrificing ecological validity

Independent and Dependent Variables

• The independent variable is manipulated to investigate its effect on another variable
• An example would be the amount of sleep in a sleep study
• The dependent variable is the outcome measured to see if it is affected by the independent variable
• An example would be the test scores
• Manipulation of the independent variable establishes if changes in it lead to changes in the dependent variable
• Experimental manipulation differentiates experimental research from correlational research

Experimental and Control Groups

• In experimental research, the population is divided into experimental and control groups
• The experimental group receives the treatment or intervention, including the independent variable
• The control group does not receive this treatment and serves as a baseline comparison
• It helps isolate the effect of the independent variable
• Control groups determine if changes observed can be attributed directly to the manipulated independent variable

Attributing Changes in the Dependent Variable

• Changes in the dependent variable can be attributed to the independent variable because the researcher uses controls to account for extraneous variables
• Researchers can isolate the effects of the independent variable by systematically manipulating the independent variable and holding other potential influencing factors constant
• This level of control enhances internal validity, enabling stronger causal claims

Extraneous and Confounding Variables

• Extraneous variables are not the focus of the study but could still influence the outcome
• If uncontrolled, they can lead to misleading conclusions
• A confounding variable varies alongside the independent variable
• It potentially causes researchers to incorrectly attribute changes
• Researchers need to control for or eliminate them to maintain integrity and ensure effects are from the independent variable
• This is crucial for valid and reliable results

Random Assignment in Experiments

• Random assignment is used to allocate participants to different groups in a random manner
• It ensures each participant has an equal chance of being assigned to any group
• It creates equivalent groups at the start of the experiment, minimizing potential differences
• This enhances the validity of causal inferences

Types of Experimental Design

• Between-Subjects Design: Different participants are assigned to each group
• An example would be the testing of two teaching methods where different students use each method
• Strength: Reduces the risk of carryover effects
• Within-Subjects Design: The same participants are used in both groups, experiencing all conditions
• An example would be assessing the same group of students sequentially under different methods
• Strength: Controls for inter-participant variability

Advantages of Using One Group as Their Own Control

• Minimizes the impact of extraneous variables, like individual differences between participants
• Variability arising from personal characteristics is controlled when the same group experiences both experimental and control conditions
• It is within-subjects design ensures observed effects are due to manipulation of the independent variable

Using Multiple Independent Variables in Experiments

• Using two or more independent variables in a single experiment is known as a factorial design
• This design allows researchers to study not only the main effects of each independent variable but also their interactions
• The variables could be a study that examines the effects of lighting (bright vs. dim) and background noise (none vs. loud)
• Lighting and noise are both independent variables and show the effect they have on aspects such as reading comprehension

Interactions Between Variables

• An interaction occurs when the effect of one independent variable on the dependent variable depends on the level of another independent variable
• In the lighting and noise experiment, bright lighting significantly improves performance only when background noise is absent but has minimal effect when the noise is loud.
• Interaction analysis uncovers more complex relationships in data

Advantages and Disadvantages of the Experimental Method

• Advantages include causal Inference through manipulation and Control by eliminating confounding factors
• Disadvantages include low Ecological Validity due to the experiments often lacking generalizability, and Ethical Constraints

Field Experiments

• Field experiments are studies conducted in real-world settings
• They aim to maintain a level of experimental control while observing participants in a natural environment
• They could involve assessing the impact of a new teaching strategy in an actual classroom
• They can offer more externally valid results, but presents challenges in controlling extraneous variables

Statistics as a Discipline

• Statistics is a scientific discipline that encompasses techniques for collecting, analyzing, interpreting, and presenting data
• It allows researchers to summarize complex data sets and draw conclusions based on empirical evidence

Statistics as Summaries of Data

• Statistics describe relationships among variables
• They assess how variables correlate and help provide insights into behavioral patterns

Descriptive statistics

• Descriptive statistics summarize the main features of a dataset, allowing researchers to grasp basic patterns or trends quickly
• They include measures of central tendency like mean,median and mode and variability such as range, variance, standard deviation
• They are essential for conveying findings clearly and understandably, and for further statistical analysis

Central Tendency

• Central tendency describes the center point or typical value of a dataset
• It summarizes the data set into a single representative value
• This concept offers insights into general trends and behaviors within the data

Measures of Central Tendency

• The mean is the arithmetic average, sensitive to extreme scores
• The median is the middle value when arranged in order and not affected by extreme scores
• The mode is the most frequent value of categorical data
• The mean is the most useful measure for normally distributed data
• The mean is unduly influenced by extreme scores
• The median becomes a valuable alternative when data distributions are skewed and provides a clear picture of the data's central tendency

Variability and Standard Deviation

• Variability refers to how data points differ from one another
• Standard deviation quantifies the variation or dispersion in a set of values
• A large value indicates that data points are widely spread out showing high variability
• A small deviation indicates that data points are closely packed around the mean, therefore showing low variability

Correlation Between Two Variables

• A correlation exists when a change in one variable is associated with a change in the other variable
• The relationship can be positive or negative

Correlation Coefficient

• The correlation coefficient indicates the strength and direction of a relationship between two variables
• Its values range from -1.00 to +1.00
• Closer to +1.00 or -1.00 signifies a stronger relationship
• A coefficient near 0 indicates a weak relationship
• In positive correlations an increase in one variable corresponds to an increase in another
• In negative correlations an increase in one variable leads to a decrease in the other

Correlation and Prediction

• Correlation relates to prediction, allowing researchers to estimate how one variable might behave based on another variable's behavior.
• Knowing the value of one can give information to the value of the other

Causation

• Correlation, even strong or perfect, does not equal causation
• A causal relationship requires there to be some correlation between variables
• Just because there is a correlation doesn't mean that is what's responsible

Examples of Positive and Negative Correlations

• A positive correlation between the number of ice creams sold and temperature exists, despite sales not causing temperature changes
• A negative correlation between number of hours spent watching TV and academic is observed

Inferential Statistics

• Inferential statistics involve methods for making generalizations about a population based on a sample
• These techniques are critical for establishing the representation probability

Replication

• Replication is the process of repeating an experiment
• It is useful in confirming previous studies
• It is vital in distinguishing between true findings and anomalies original studies
• Publishing replication can be challenging as journals often prioritize novel research

Sample and Population

• A population is the complete set of individuals or instances that researchers are interested in studying
• A sample refers to a subset of the population that is selected
• A sample allows researchers to draw conclusions about popuilation

Sampling Bias

• Sampling bias occurs when the sample collected is not representative of the population
• Representative a sample accurately mirrors the characteristics of the population
• Biased samples do not reflect representation which can lead to potential distortions in the research outcomes

Placebo Effect

• The placebo effect is when participants experience perceived improvements due to their expectations
• The effect can occur even when there is an inactive treatment

Social Desirability Bias

• Social desirability bias is a tendency of participants to provide answers they believe are more socially acceptable
• It can distort self-reported data
• Response Set can be a pattern where participants consistently answer in a specific way, for example always choosing favourable options can lead to biased results
• Halo effects refers to a researcher’s overall impression of a participant influencing evaluations
• Biases primarily affect the participants, and consequences can extend to researchers

Experimenter Bias

• Experimenter bias is the influence that a researcher’s expectations can have on a study's outcome
• This can manifest through subtle cues and potentially skew results
• A double-blind procedure is an effective method for minimizing both types of bias and participant bias
• Here neither participants nor researchers know who's receiving what