CHAPTER ONE
- Behavioral research is empirical
- Everyday science (regular people developing own theories through observation) vs. empirical research
- Behavioral research is not intuition/common sense, people w the latter can become convinced of things like ESP w/o evidence, it’s been found that there are many cognitive/motivational biases that lead us to erroneous conclusions
- Tendency to think we could’ve predicted something (we probably couldn’t have) is called hindsight bias
- Scientific method (collecting, organizing analyzing data), assumptions, rules and procedures, must be objective
- Values (opinions, can’t be proved/disproved as true/false) versus facts (objective proved through empirical evidence)
- Of course, facts can influence values
- Facts and values difference not always clear; many ways to interpret data (see race-related IQ differences), can have bias involved
- Values are still important - gotta make clear which parts are objective and which aren’t (research report)
- Basic research is about fundamental behavioral questions (cognitive psychs study how dif types of practice influence memory, biological psychs study how nerves conduct impulses from skin receptors to the brain, this is to acquire better knowledge on behavior)
- Applied research investigates issues that have implications for everyday life and provide solutions to everyday problems (what types of psychotherapy are most effective in reducing depression, what types of ad campaigns will reduce drug and alcohol use, factors associated w success in various fields, etc)
- Program evaluation research conducted to study the effectiveness of methods designed to make positive social changes, such as training programs, anti prejudice programs and so on
- Applied and basic research inform each other; basic research provides underlying principles that can be used to solve specific problems while applied research gives ideas for the kinds of topics that basic research can study
- RESEARCH DESIGNS:
- Descriptive - assessing the current state of affairs, provides a snapshot of things. One type is surveys and interviews, showing us what people are thinking/feeling/doing at a certain point in time vis a vis “current concerns” in a city/state/nation.
There is also naturalistic observation, based on observation of everyday events (developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research as would a biological psychologist observing animals in their natural habitats or a sociologist studying the way people use public transportation in a large city
Qualitative (observing & describing events as they occur w the goal of capturing all the richness of daily behavior and the hope of discovering and understanding phenomena that might’ve been missed if only more cursory examinations had been used, data like field notes and/or audio/video recordings) vs. Quantitative (questionnaires, systematic observation of behavior which is designed to be subjected to statistical analysis. Strength of qualitative: vividly describes ongoing behavior in its original form, but more subjective bc no stats. Both together are good, informative.
- Descriptive research used to provide relatively complete understanding of what’s currently happening, but is limited to static pictures, can’t tell us how concerns developed
- Correlational - measurement of two or more relevant variables and an assessment of the relationship between or among them, goal of uncovering variables that show systematic relationships w each other
- Pearson Product-Moment Correlation Coefficient: r = -1.00 to r = +1.00. Positive values indicates positive correlations, in which people who are farther above average on one variable (for instance height) generally are also farther above average on the other variable (for instance weight). Negative values of r indicate negative correlations in which people who are farther above average on one variable (for instance study time) generally are also farther below average on the other variable (memory errors). Values of the correlation coefficient that are farther from zero (either positive or negative) indicate stronger relationships, whereas values closer to zero indicate weaker relationships.
- Correlation used to make predictions.
- Strengths: used to assess daily behavior (closer in class = better grades).
- Limitations: correlation doesn’t equal causation
- Experimental - creation/manipulation of given situation/experience for two or more groups of individuals, followed by a measurement of the effect of those experiences on thoughts, feelings, behavior
- Designed to create equivalence between individuals in Dif groups before the experiment begins so any differences found b/w them can confidently be attributed to effects of experimental manipulation
- Strengths: demonstrates, draws conclusions about causal relationships
- Limitations: can’t be used to study important social questions (eg violence) bc conditions cannot be manipulated (descriptive/correlational used for these instead)
- Can be effective to use dif research designs together: using more than one technique/research design to study the same thing, with the hope that all of the approaches will produce similar findings, is known as covering operations, it is common to do so in the behavioral sciences
- Questions at the end


CHAPTER TWO
- One way to get ideas for research is to develop an applied research project w the goal of producing better understanding of the causes of, or potential solutions to, everyday problems
- Inductive method: your own curiosity becomes the source of your ideas (you noticed your friends have had trouble developing satisfactory romantic relationships), you have a theory, you want to test it, we want to test our hunches, Freud developed theory of personality by observing his patients, Piaget developed theory of cognitive development by watching his own children)
- Based on intuition alone that doesn’t relate to existing scientific knowledge risks not advancing the field very far
- More links you can draw b/w your research and existing research, greater the likelihood your research will make an important contribution to the field)
- Finding limiting conditions of previous research (people believed women were likelier to conform to others’ opinions than men, then scientists began to consider the types of tasks that had been used in conformity research was a basic limiting condition found, previous research had relied to an extent on topics like baseball which men were more knowledgeable, was too broad a conclusion - women actually conform more when they believe men know more
- Explaining conflicting findings
- Literature search
- Locating sources of information
- Conducting the search
- Investigating computer databases, using keywords effectively, using abstracts to select important documents
- Formalizing ideas into research hypotheses
- Principles that are so general as to apply to all situations are known as laws
- Theory is an integrated set of principles that explains and credits many but not all observed relationships within a given domain of inquiry. Process of using a theory to generate specific ideas that can be tested through research is known as the deductive method
- Good theories are general, summarizing many dif outcomes; parsimonious, provide simplest possible account of those outcomes. Stage theory of cognitive development accounts for developmental changes across a wide variety of domains, does so simply by hypothesizing a simple set of cognitive stages.
- Good theories provide ideas for future research, and are falsifiable (variables of interest can be adequately measured, relationships b/w variables that are predicted by the theory can be shown through research to be incorrect)
- Theories in which the variables can’t be measured or in which the variables are vague enough that they cannot provide information to falsify the theory are called tautological.
- A theory only survives to the extent that it’s good enough and no currently known alt theory is better, when a better theory is found it’ll replace the old one, this is part of the accumulation of scientific knowledge
- Research hypothesis can be defined as a specific and falsifiable prediction regarding relationship b/w or among 2+ variables
- Independent variable (experimental manipulation) vs. dependent variable (variable caused by independent variable)
Research hypothesis is that manipulation IV will change DV
- In correlational research designs, both IV and DV are measured. Furthermore, bc it’s not possible to state the causal relationships b/w variables in correlational designs, the terms IV and DV are sometimes replaced with the terms predictor variable and outcome variable, respectively
- PRIMARY SOURCES: research reports that contain complete descriptions of collected data and data analyses, they appear in professional journals
- SECONDARY SOURCES: documents that contain only summaries/interpretations of research reports rather than a complete description of them (textbooks, books written by one author, edited books containing collection of chapters on one topic from different authors). Some journals (Psychological Bulletin, Annual Review of Psychology), also publish primarily secondary-source articles

CHAPTER THREE
Four basic goals/principles of ethical research: PRINCIPLE ONE: Minimising the risk of harm. PRINCIPLE TWO: Obtaining informed consent. PRINCIPLE THREE: Protecting anonymity and confidentiality. PRINCIPLE FOUR: Avoiding deceptive practices.

Informed consent is a crucial part of enrolling in a clinical trial because it gives the potential participant all the information they need to understand what they are volunteering for. ... It is very important for people thinking about participating in a clinical trial to understand their role in the study.
Informed consent entails that participants should receive detailed information on the research they are participating in, so that they can make a voluntary, informed and rational decision regarding whether or not to participate in such research.
The following are the required elements for documentation of the informed consent discussion: (1) the nature of the procedure, (2) the risks and benefits and the procedure, (3) reasonable alternatives, (4) risks and benefits of alternatives, and (5) assessment of the patient's understanding of elements 1 through 4.

Deception is the intentional misleading of subjects or the withholding of full information about the nature of the experiment. Investigators may mislead or omit information about the purpose of the research, the role of the researcher, or what procedures in the study are actually experimental.
Active deception involves intentionally providing inaccurate or false information to subjects. Examples include: In order to induce stress, study personnel tell subjects that they will give a speech that evaluators will observe on video, when the subjects' speeches will not actually be recorded or observed.

Debriefing means providing information about the research to participants after they have. given their informed consent to participate, and usually after their participation is completed. A. Debriefing Form is required if the research involves deception of the participants.

Under FDA regulations, an Institutional Review Board is group that has been formally designated to review and monitor biomedical research involving human subjects. In accordance with FDA regulations, an IRB has the authority to approve, require modifications in (to secure approval), or disapprove research.
At UNH, the primary purpose of the Institutional Review Board for the Protection of Human Subjects in Research (IRB) is to protect the rights and welfare of human research subjects by ensuring that physical, psychological, legal, and/or social risks to subjects are minimized, and when present, justified

CHAPTER FOUR
Conceptual variables vs. measured variables
In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

An operational definition is how we (the. researcher) decide to measure our the variables. in our study (variable = anything that can be measured). ◦ There are usually hundreds of ways to measure a DV (e.g. behavior).
a description of something in terms of the operations (procedures, actions, or processes) by which it could be observed and measured. For example, the operational definition of anxiety could be in terms of a test score, withdrawal from a situation, or activation of the sympathetic nervous system.
An operational definition is just a decision about operations to measure something

A nominal scale describes a variable with categories that do not have a natural order or ranking
A nominal variable is a type of variable that is used to name, label or categorize particular attributes that are being measured. It takes qualitative values representing different categories, and there is no intrinsic ordering of these categories.

Nominal, Ordinal, Interval, and Ratio are defined as the four fundamental levels of measurement scales that are used to capture data in the form of surveys and questionnaires, each being a multiple choice question.

1. Nominal scale of measurement
The nominal scale of measurement defines the identity property of data. This scale has certain characteristics, but doesn’t have any form of numerical meaning. The data can be placed into categories but can’t be multiplied, divided, added or subtracted from one another. It’s also not possible to measure the difference between data points.
Examples of nominal data include eye colour and country of birth. Nominal data can be broken down again into three categories:
* Nominal with order: Some nominal data can be sub-categorised in order, such as “cold, warm, hot and very hot.”
* Nominal without order: Nominal data can also be sub-categorised as nominal without order, such as male and female.
* Dichotomous: Dichotomous data is defined by having only two categories or levels, such as “yes’ and ‘no’.
2. Ordinal scale of measurement
The ordinal scale defines data that is placed in a specific order. While each value is ranked, there’s no information that specifies what differentiates the categories from each other. These values can’t be added to or subtracted from.
An example of this kind of data would include satisfaction data points in a survey, where ‘one = happy, two = neutral, and three = unhappy.’ Where someone finished in a race also describes ordinal data. While first place, second place or third place shows what order the runners finished in, it doesn’t specify how far the first-place finisher was in front of the second-place finisher.

3. Interval scale of measurement
The interval scale contains properties of nominal and ordered data, but the difference between data points can be quantified. This type of data shows both the order of the variables and the exact differences between the variables. They can be added to or subtracted from each other, but not multiplied or divided. For example, 40 degrees is not 20 degrees multiplied by two.
This scale is also characterised by the fact that the number zero is an existing variable. In the ordinal scale, zero means that the data does not exist. In the interval scale, zero has meaning – for example, if you measure degrees, zero has a temperature.
Data points on the interval scale have the same difference between them. The difference on the scale between 10 and 20 degrees is the same between 20 and 30 degrees. This scale is used to quantify the difference between variables, whereas the other two scales are used to describe qualitative values only. Other examples of interval scales include the year a car was made or the months of the year.

4. Ratio scale of measurement
Ratio scales of measurement include properties from all four scales of measurement. The data is nominal and defined by an identity, can be classified in order, contains intervals and can be broken down into exact value. Weight, height and distance are all examples of ratio variables. Data in the ratio scale can be added, subtracted, divided and multiplied.
Ratio scales also differ from interval scales in that the scale has a ‘true zero’. The number zero means that the data has no value point. An example of this is height or weight, as someone cannot be zero centimetres tall or weigh zero kilos – or be negative centimetres or negative kilos. Examples of the use of this scale are calculating shares or sales. Of all types of data on the scales of measurement, data scientists can do the most with ratio data points.



Self-reported measures are measures in which respondents are asked to report directly on their own behaviors, beliefs, attitudes, or intentions. For example, many common measures of attitudes such as Thur-stone scales, Likert scales, and semantic differentials are self-report.
In psychology, a self-report is any test, measure, or survey that relies on an individual's own report of their symptoms, behaviors, beliefs, or attitudes. Self-report data is gathered typically from paper-and-pencil or electronic format, or sometimes through an interview.
Researchers have found that self-reported data are accurate when individuals understand the questions and when there is a strong sense of anonymity and little fear of reprisal.” “These results are very similar to those found in other surveys as well as results gathered historically.
d) The three commonly used self-report measures for assessment of personality are Eysenck Personality Questionaire (EPQ), Minnesota Multiphasic Personality Inventory (MMPI) and Sixteen Personality Factor Questionnaire (16 PF).
The main advantage of self-report is that it is a relatively simple way to collect data from many people quickly and at low cost. A second advantage is that self-report data can be collected in various ways to suit the researcher's needs.

Behavioral measures examine actions or manners exhibited by the user that are responses to objects or events in the virtual environment. For example, does the user duck if a virtual object is thrown at his head.

Experimental designs with more than one independent (manipulated) variable are known as factorial experimental designs. Factor refers to each of the manipulated independent variables. Experiments with one IV = one-way designs, two IVs = two-way designs, and so on.

In a factorial design, each level of one independent variable is combined with each level of the others to produce all possible combinations. Each combination, then, becomes a condition in the experiment.

This particular design is referred to as a 2 × 2 (read “two-by-two”) factorial design because it combines two variables, each of which has two levels.
If one of the independent variables had a third level (e.g., using a handheld cell phone, using a hands-free cell phone, and not using a cell phone), then it would be a 3 × 2 factorial design, and there would be six distinct conditions
Level first, IV second

When means are combined across the levels of another factor (mean for avg score for children violent / nonviolent cartoons), they are said to control for/collapse across the effects of the other factor and are called marginal means. Differences on the dependence measure across the levels of any one factor, controlling for all other factors in the experiment, are known as the main effect of that factor. (Test by controlling for conditions of cartoon, children who’d been frustrated vs. not.)

Interaction is a pattern of means that may occur in a factorial experiment design when the influence of one IV on the DV is dif at dif levels of another IV or variable.

The effect of one factor within a level of another factor (ie the effect of viewing violent vs. nonviolent cartoons for frustrated kids) is known as a simple effect of the first factor.

Three-way design has three two-way interactions (tests same hypothesis as original 2x2 bc collapses over sex of child).
Three-way interaction tests whether all three variables simultaneously influence the dependent measure.

A mixed factorial design involves two or more independent variables, of which at least one is a within-subjects (repeated measures) factor and at least one is a between-groups factor. researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone (while counterbalancing the order of these two conditions).

Post hoc (“after this” in Latin) tests are used to uncover specific differences between three or more group means when an analysis of variance (ANOVA) F test is significant.

Threats to internal validity are essentially threats to causal control. They mean that we do not know for sure what caused the effects that we observed. Naturally, we like to hope that our interventions (experimental treatments) or other known and measured independent variables caused the effects.

Eight threats to internal validity have been defined: history, maturation, testing, instrumentation, regression, selection, experimental mortality, and an interaction of threats.



Experimental research maximizes internal validity, but practically/ethically can’t always manipulate its IV(s).

Quasi-experimental sidesteps this (education, human development, social work, clinical psych) by making comparisons among different groups but can’t randomly assign individuals to those groups. Can be between participants (scholastic achievement of autistic vs. non autistic children) or repeated measures (comparison of mental health of individuals before and after they’ve participated in a psychotherapy program).

Quasi-experimental bc it’s correlational (IV(s) are measured not manipulated), but also similar to experimental (IV involves grouping, data analyzed with ANOVA).

Program evaluation research is designed to study intervention programs, such as after-school programs, clinical therapies or prenatal-care clinics, with the goal of determining whether the programs are effective in helping the people who make use of them. Not gonna have much control, threats to internal validity (variables may cause both the IV and DV).

Single-group: group of participants measured after they’ve had experience of interest (threat: no control group).

Comparison-group: group expected to be similar but not equivalent to the experimental group (random assignment has not been used). (Threat: differences could’ve existed before.)

Selection threats (willful, not random assign)

Single-group before-after (self-evident)
Threats: retesting threats (hypothesis might be guessed, dif answers)

Attrition threats: those who stick around might be different
Maturation/history threats: potential changes over time unrelated to IV

Comparison-group before-after (quite good, though not COMPLETELY immune, esp to history threats - but still v good)

Participant-variable designs: when grouping variable involves preexisting characteristics of participants. Variable differs across participants = participant variable. Single-participant designs