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Chapter 1 Science and Common Sense (CS) Science is a systematic and controlled version of common sense Differences between science and common sense: 1) Science systematically build theoretical structures while realizing that such theories are man-made and thus may not be closely related to reality vs. CS which loosely uses theories and concepts 2) Scientists systematically and empirically test theories and hypothesis, and carefully guard against selectively testing only theories and hypotheses that confirm their biases and expectations vs. CS where select to confirm initial beliefs (i.e., confirmatory bias) 3) Scientists systematically control variables for causes that are not those that are hypothesized i.e., confounds vs. CS where laypeople seldom control their explanations systematically 4) Scientists consciously and systematically pursue relationships among phenomena (and then test them systematically) vs. CS- don`t systematically pursue relationships, take fortuitous relations and reduce them to cause and effect 5) Scientist attempts to explain phenomena in a way that is testable and can be observed and rule out untestable i.e., metaphysical explanations vs. CS- don`t rule out the metaphysical Charles Sanders Peirce’s 4 methods of knowing (what Piece called fixing belief): 1) Method of tenacity: people hold firmly to the truth because they’ve always believed it to be the truth 2) Method of authority: believing in “established beliefs”, i.e. from others, particular authority; superior method to tenacity because there can be slow progress through findings 3) A priori method/method of intuition: propositions that “agree with reason” and not necessarily with experience; assumes people have natural inclinations toward truth. Difficulty lies in—whose reason is right? 4) Method of science: beyond human beliefs or opinions, whereby the method can yield conclusions that are the same for every man regardless of their biases. Includes the characteristic of self-correction/build-in checks along the way to be unbiased. Thus, the scientific belief moves beyond reason and opinions and assume as much as is possible, an objective stance.

Science and its functions: 2 broad views of science:

1) Static view: science as an activity that contributes systematized information to the world. Emphasis is on the present state of knowledge and adding to it- i.e., fact accumulation 2) Dynamic view/heuristic view: science as an activity that scientists perform, emphasis on theory and approaches that are fruitful for further research; discovery 2 views on function of science: 1) Science as a discipline or activity aimed at improving things/making progresscenters on practicality and payoff 2) Science is to establish general laws/theories that explain phenomena to derive a sense of predictability about the world (dynamic view + theory development) Sampson’s 2 opposing views of science (this is the table on Page 10): 1) Conventional/traditional perspective: Goal: to describe the reality of human interactions and functions Philosophical position: Reality can be discovered through objective observation Metaphorical statement: Science as a mirror of nature that reflects things as they really are Methodological considerations: Methods are created to control or eliminate factors that would weaken the researchers ability to discover the true nature of reality. 2) Nontraditional/sociohistorical perspective: Goal: describing the variety of human experience and activity through social and historical info and the roles that they (sociohistorical factors) play in human life Philosophical position: Reality can only be discovered from a particular standpoint, the observer is always positioned vis-à-vis the phenomenon under study (i.e., cannot be objective) Metaphorical statement: Science is a storyteller and gives different accounts of reality as it is understood. Methodological considerations: The researcher’s understanding of reality is shaped by broad social and historical factors, the methods can yield a richer and deeper understanding of reality based on access diverse accounts of how people make meaning of their experiences.

Aim of science, scientific explanation, and theory - Basic aim of science is theory i.e., to explain natural phenomenon, not the betterment of humanity - Definition of theory: a set of interrelated concepts that present a systematic view of phenomena by specifying relations among variables, with the purpose of explaining and predicting the phenomena. In other words, by explaining the extent and nature of relations between variables, we can make predictions.

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NB: Predictions are made possible through the presence of control (eliminating confounds and sources of error) A good theory is modest, limited, and has specific research aims  A theory is thus a tentative explanation that won’t fit all observations. In other words there will always be contradictions or alternative explanations. Simple explanation is preferred- i.e. parsimony (Occam’s Razor) Scientific research: a systematic, controlled, empirically tested, amoral, publically scrutinized through peer-review, and critical investigation of natural phenomena, guided by theory and hypotheses about the presumed relations among such phenomena

The scientific approach The scientific approach is a special systematized form of reflective thinking and inquiry Based on Dewey’s analysis these are the stages of the scientific approach: a. Problem-obstacle-idea: Person encounters a problem and an obstacle to understanding the problem which gives rise to voicing concerns and expressing the problem in the form of an idea b. Hypothesis: After referring to past experiences to explain phenomena, the person makes a tentative claim about the hypothesized relation between the phenomena that he/she wishes to understand further c. Reasoning-deduction: deducing the consequences/implications of the hypothesis (deductive: moving from broader picture to more specific one). Often, the problem changes during this stage as once implications are deduced, may refine problem/hypothesis. Could also use inductive reasoning (using particular facts and moving toward a general statement/hypothesis)- problem is confirmatory bias- looking to confirm broadly what you think specifically. d. Observation-test-experiment: test relation between variables (i.e., putting the problem relation to empirical test). NB: We are not testing the variables or hypotheses directly but the implications re the relations between them, so then we can make inferences about the tested relation. NB: Dewey’s 4 stages are not linear but more cyclical, we are always going back and forth between them Chapter 2 -

Science is not simply a fact-gathering activity because we always have preconceived notions; therefore need to ground science in a hypothesis that is based upon theory so we know what facts to gather rather than allowing our preconceived notions to guide the research

Research problems: - A problem is a statement in an interrogative form (i.e. a question) that asks “what relationship exists between 2+ variables?  3 criteria of a good problem: 1. Problem should express a relationship between 2+ variables

2. Should be stated clearly and unambiguously in question form 3. Statement should imply possibilities of empirical testing i.e., relational statement should be made in a way that points to how the relationship between 2+ variables will be measured Research hypotheses - Hypotheses: conjectural/tentative statements of the relationship between 2+ variables in declarative form - 2 criteria for good hypotheses 1) Hypothesis as statement about the relations between variables 2) Hypothesis carry clear implications for testing the stated relations/measuring variables – therefore must define our variables to they are amenable to being tested/measured - 3 reasons why hypotheses are important tools of scientific research 1) They are working instruments of theory (i.e., they can be deduced from preexisting theory and inform future theory) 2) They allow relational propositions to be tested which can then be shown to be probably true or false (leading to predictions) 3) They are tools for advancement of knowledge b/c it allows non-bias of thinkers

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Problems and hypotheses Pros of problems and hypotheses: 1) They direct investigation and inquiry because the relations expressed in them tell you what to do/how to test relations 2) They allow deductions to occur from implications of hypotheses and problemsi.e., because they are generalized relational statements you can deduce how to empirically test implications Differences between problems and hypotheses  Hypotheses can be tested, problems are only tested through hypotheses. In other words, you can only test a problem when it is reduced to its hypothesis, cannot test the problem itself. Values and definitions and their intersection with problems: 1) They are not ethical questions/value questions (no should, better than, ought)- i.e., not value statements 2) Problems that are too general/vague in their definition cannot be tested 3) Some state problem as methodological points, even though should point to how it will be tested, your problem should not involve techniques or design methods Generality vs. specificity- want to balance the two- if it’s too general then can’t be tested, if too specific- then limits generalizability because you “narrow the problem out of existence” but an optimal amount of specificity clarifies how you will test it. Probs/hyps need to reflect multivariable and complex nature of reality

Chapter 3 Scientists operate on 2 levels: theory-hypothesis-construct and observation- and they often shuttle back and forth between these two levels - Must define constructs before observing so that observation is possible - Concept: expresses an abstraction formed by generalization from particulars, i.e. weight is an abstraction based on particulars which include mass, heavy/light, more/less etc. - Construct: concept that is consciously invented for scientific purposes - Variables: property that assumes different values i.e., a variable varies – it can be dichotomous (male/female) or polytomies- a continuous variable can be converted into a dichotomy or polytomy (e.g. depression- can be converted into high or low or high, low, moderate) Constitutive and operational definitions of constructs and variables - Constitutive definition of construct: defines a construct using other constructs - Operational definition assigns meaning to constructs by specifying the activities/operations necessary to measure it, and evaluate the measurement  2 types of operational definitions 1. Measured: describes how variable will be measured (not manipulated) 2. Experimental: states details (operations) of the experimenter’s manipulation of a variable, i.e. reinforcement can be operationally defined by giving details of how subjects will be rewarded NB: Operational definitions bridge theory-hypothesis-construct and observational levels but are limited because we can never get at the full meaning of the construct through the variable that operationalizes it. It is more common to indirectly access the construct through measuring or manipulating it indirectly and then drawing conclusions from these indirect approaches. So the scientist shuttles back and forth between the two levels by operationally defining the construct for the current study then ascertaining the relation between the operational definition and the measured variables and then drawing inferences about the relations between the constructs. Some distinctions and terms - Variables: symbols to which numerals/values are assigned - Dichotomous/binary variables: only 2 values, i.e. 1 or 0, yes or no - Polytomous variable: more than 2 values - Continuous variable: taking on an ordered set of values within a certain range - Categorical variable: grouped by either having or not having the characteristic of subset - Independent variable: presumed cause of the dependent variable(antecedent)- must have at least two levels that are manipulated (e.g., control vs. experimental) - Dependent variable: the presumed effect (consequent), outcome measure, the condition we are trying to explain

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Continuous variable: rank ordered set of values within a range of infinite possibilities Categorical variables: nominal- all or nothing, no rank order Experimental =manipulated variable=active variable=stimulus variable: any variable that is manipulated in experiment (IV) Measured variable=attribute variable=response variable: variables that can’t be manipulated are attribute/subject characteristic variables/organismic variables/individual differences Latent variable: unobserved, presumed to underlie observed measured variable, i.e. intervening or underlying construct (vs. observed variable) – e.g. motivation- is a construct that can only be inferred based on its measured variable counterpart- so motivation is an ‘in the head variable’- it is a construct that accounts for an unobservable psychological process that we measure indirectly through the measured variable.

From abstract (not measureable) to specific (measureable): Concept  construct  variable To make inferences: concept  construct  variable Chapter 4 Sets  Sets and their elements are the primitive materials under which mathematics operates.  Set theory provides a clear definition of relations because it guides how we understand probability and sampling and is highly related to logic and how we categorize the world  Definition: a set is a well-defined collection of objects or a group of objects i.e., it clearly states whether a given object belongs to a set 2 ways to define sets: a) lists definition- list all members of a given set b) rule definition: this is used in research- it is a rule determining whether objects do or don’t belong in a set (think inclusion and exclusion criteria) Subsets  Subset of a set is a set that results from selecting sets from a original set. In other words, each subset is part of the broader original set (e.g. females and males are subsets of the original set of people) NB- subset is to sample as set is to population i.e., a sample is a subset of the original population Set Operations  Intersection is the overlapping of two or more sets; it is the elements shared in common by the two or more sets. The intersection (denoted as ∩) of two sets A and B is the set that contains all elements of A that also belong to B (or equivalently, all elements of B that also belong to A), but no other elements. E.g. Some females that are also democrats  Union of two sets is a set that contains all of the members of A and all of the members of B. In other words, we add the elements of A to be to form the new set A ∪ B – e.g. putting males and females together to form our new set

Universal and Empty Sets; Set Negation  The universal set is the set of all elements under discussion. It can be called the universe of discourse or level of discourse. Labeled U.  The empty set is the set with no members in it; it can also be called the null set. E.g., A∩B=E- this means that there is no intersection or overlap between A and B  The negation or complement of a set A is written ~A. It means all members of U that are not in A. E.g., If A is all men then U= ~A refers to only women. We can also state that A ∩~A= E which means you are either a male or a female and aren’t both. Set Diagrams If circles are separate within rectangle- the set includes both circles but there is no overlap – refers to union If circles overlap- this refers to their intersection Partitions and cross partitions - This is basically how U (universal set) can be broken down or partitioned into all subsets that don’t intersect and exhaust all of U aka disjointed and exhaustive i.e., A U B=U and A ∩B=E. Cross-breaks or cross-tabs are basically tables that show how subsets relate to one another and to U (universal set) i.e., it shows how a subset can be partitioned and then combined with another– for example anxiety and achievement (U’s) can be related to one another such that there are four combinations or subsets (high-high, low-low, low-high, high-low) Levels of Discourse  We must be careful not to mix or shift our levels of discourse (from U to subsets of Uessentially, don’t shift from U-population to A-sample), or to do so only knowingly and consciously. Set-thinking helps us avoid such mixing and shifting.  Research requires precise definitions of universal sets. Precise means to give a clear rule that tells you when an object is or is not a member of U.  The set idea is fundamental in human thinking. This is because all or most thinking probably depends on putting things into categories and labeling categories. Chapter 5 Relations  Relations are the essence of knowledge. Almost all science pursues and studies relations. - Relations give meaning to a concept because they help us arrive at a relativist understanding of natural phenomena, because understanding concepts in isolation doesn’t further knowledge. Relations as Sets of Ordered Pairs  Relations in science are always between classes or sets of objects.  We know the relation by a process of abstraction from sets of characteristics (e.g. marriage is abstracted from sets of husbands and wives)

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Relation is a set of ordered pairs. Ordered pairs are two objects, or a set of two elements, in which there is a fixed order for the objects to appear (e.g. marriage is a set of the ordered pair of wife, husband, wherein wives come first and each husband can only be paired with his wife so there is a fixed nature to the pair). On a graph, the ordered pairs would be expressed by a point showing the relation between X (wife) and Y (husband)- drawing a line through the plotted points shows us the magnitude and direction of the relation Domain is always subset A (1st set- e.g. wife). The Range is always subset B (2nd set- e.g., husbands). A special type of relation is a function. A function connects elements of the domain and the range. A relation is a function when each element of the domain is paired with one and only one member of the range. E.g. being a husband (range) is a function of having only one wife (domain)- being a mother is not a function- because can have more than one child Determining relations in research- pair each individual member of A (domain) with B (range), then we obtain all possible pairs between the two sets which is called the Cartesian product A X B. All possible pairs is based on a rule (e.g. marriage is a rule that includes all married people but not all males and females) Rules of correspondence: This is a formula that tells us how to map objects of one set onto the objects of another- e.g. D(omain) = (Heidi, Bram, Kristin, Jesse, Dave, and Lindsay), R(ange)= (female=0, male=1) – so members of the domain (i.e. couples) are mapped onto the range (gender) according to some rule (assign 0 for female and 1 for male)

Some Ways to Study Relations  Graphs (two-dimensional plots), tables, and graphs and correlation (where the correlation i.e. relation is a statistic/numerical value)  Tables of means- compares means along the dependant variable or the differences in outcome due to independent variable- shows separate and combined effects of independent variable on dependant variables- these comparisons indicate differences in effects on the dependant variable which are the relations. The relation is expressed as R (correlation coefficient) shows how x and y (ordered pairs of sets or domain and range, respectively) covary. Multivariate relations and regression  NB: often more than two variables at work (i.e. beyond if p then q)- logic of multivariate inquiry- if p then q under conditions r- more than one independent variable relating to the dependant variable – expressed in a path diagram showing direct and indirect influences of each independent variable e.g. Social class (x1) influences School achievement (y1) given/under conditions/throug...