1
Q
The Scientific Method
A
- the formal methods, primarily the set of techniques and concepts, used to examine & answer questions of a scientific nature
1) Construct a theory
2) Generate a hypothesis
3) Choose a research method
4) Collect data
5) Analyze data
6) Report the findings
7) Revise existing theories
- purpose: minimize biases, conflicts, & oversights
2
Q
A Theory is
A
a general set of ideas about the way the world works
- A good theory must be able to encompass all the known facts, not just some of them. –>. If we modified the theory to state, “all four-legged animals, except the gecko lizard, are warm-blooded”, this doesn’t really do the job. We need a theory of ‘warm-bloodedness’ that explains why gecko lizards, while being four-legged animals, are not warm-blooded whereas all other four-legged animals are warm-blooded.
3
Q
A hypothesis is
A
a testable statement that makes specific predictions about the relationship between variables involved in the theory
4
Q
Research methods
A
- determine the way in which the hypothesis will be tested
5
Q
Collect Data
A
- take measurements of the outcomes of the test
6
Q
Analyze Data
A
- Understand the data & discover trends or relationships between the variables
- scientists decide whether or not to accept or reject the hypothesis
7
Q
Report findings
A
- scientists report findings the findings of their research to others by making formal presentations
- Publish articles in scholarly journals
- Fortunately, research in psych is a large & collaborative enterprise with several checks that can minimize these errors.
- This begins with the peer review system used for publishing findings in scientific journals. –>Before publishing, a submitted study will be sent out for review by a number of experts in the field.
8
Q
Revise Theories
A
Incorporate new info to our understanding of the world
- science & research r dynamic processes, theories always need to be revised with new info
9
Q
Paradigms shift is
A
a fundamental change in a fundamental model or perception of events, where a new way of thinking or doing something replaces the old, often dominant, way. (google)
- Ex, in 1543, when Copernicus challenged the existing dogma that the earth was at the center of the universe
10
Q
Anecdotal evidence is
A
evidence gathered from others’ or ones’ own experience.
11
Q
What’s wrong with only drinking the energy once yourself and drawing a conclusion?
A
1) can’t be sure that ur single experience is representative of the general result that would occur –> ex if u drank the drink multiple times ur results may be diff
2) can’t be sure that ur experience is the same as what others would experience under the same circumstances
3) can’t even be sure that any change in ur test performance was due to the energy drink u had at all –> maybe this test was just easier or harder
12
Q
Experiment
A
scientific tool used to measure the effect of one variable on another
- scientist manipulates the independent variable and observes the effect on the dependent variable
13
Q
A variable is
A
anything free to take on diff (at least 2) values
–> it is critical that we work with operationally defined variables
14
Q
Binary Variables are
A
variables that only have 2 possible values.
–> ex, alive/dead, left/right, on/off, night/day, etc)
15
Q
A constant is
A
A feature or quality that always takes the same value across all situations.
- NOT a variable
16
Q
Independent variable
A
variable manipulated by the scientist
17
Q
Dependent variable
A
Variable observed my the scientist
18
Q
in it’s simplest form, an experiment can have 2 groups of participants. Elaborate
A
1) Experimental group: will receive a manipulation of the independent variable
2) Control group: no manipulation of independent variable given
- ideally, participants in the experimental & control group should be as similar as possible , minimizing the diffs that exist between them before the experiment
–>this way, if a diff in the dependent variable is found, it’s likely due to the manipulation of the independent variable
19
Q
A within participant experimental design
A
- 1 way to guarantee that the experimental & control groups are as similar as possible
- This technique tests the same subject repeatedly while the independent variable is manipulated
- Ex, Eric takes some tests with the drink and some without, comparing the results. –> he would be his own experimental & control group
20
Q
Issue with a within participant design
A
1) cuz the same particpant needs to be tested repeatedly, it can be time consuming & costly to have a participant complete the entire experiment
2) the measure we r using, or the participant themselves, may change in important ways during the course of the experiment. –> ex, Eric may show improved performance on each test throughout the semester
21
Q
The practice Effect
A
An improvement in performance over the course of an experiment as a result of experience–> separate from the effect of the independent value
- practice effects can reduce the control of our experiment cuz it’s hard to separate this natural improvement from the effects of manipulating the independent variable
22
Q
Between Participant Experiment
A
- one group receives the experimental manipulation while the other acts as the control group
–> Ex, Eric’s idea of comparing test score with his roommate, where he takes the energy drink and his roomate doesn’t
23
Q
Confounding Variable
A
- aka extraneous variables
- A variable associated with an independent variable that complicates the effects of the independent variable on the outcome (dependent variable).
- essentially variables that the researcher did not manipulate or measure, but could still affect the outcome of the experiment –> hence, why we have control groups
- leads to questionable results as the result of the experiment cannot be solely attributed to the manipulation of the independent variable
–> Ex, if everyone in one group is non-veg and veg in the other, diet may also play a role as well as energy drink usage
24
Q
A good experiment seeks to control for the effects of all possible _______________________. Elaborate
A
extraneous/confounding variables
- In a sense, we will seek to turn any potential EV we discover into a constant.
–> This raises one of the difficulties of applying the experimental method; every EV we turn into a constant has the potential to limit the scope of our results
- Another option is to equate our groups with respect to the EV, rather than eliminating it completely. In this case, we could ensure that each group had an equal number of males and females.
25
What is wrong with picking a very specific group of people for an experiment?
- u limit the scope of the experiment in terms of making generalizations about a larger propulations
- ex, if you only test on blonds, you can't say it works the same way on brunnettes
26
Population is
the general group we are trying to learn about
- population doesn't always refer to the entire human race
-The important consequence is that you can only generalize your conclusions to the population from which your sample came.
27
Sample is
the selected members of the population that we actually collect data from
- the sample must accurately reflect (represent) the population itself so that results can be generalized --> Ideally we draw a random sample --> choosing a random sample from the entire population reduces the chance that selections might be biased towards a specific group
--> helps eliminate any pre-existing diffs between control & experimental group
28
Random Assignment
- assigning participants to either the experimental or control group randomly
--> helps avoid any biases that might cause the participants in the groups to differ from each other --> helps eliminate any pre-existing diffs between control & experimental group
--> helps us combat the effects of unknown EVs
29
The placebo effect
- The situation where an individual exhibits a response to a treatment that is not due to its real therapeutic effects
- Ex, when patients have shown remarkable recovery from illness when given drugs that were presented as "miracle cures", even when does drugs were known to have no effect.
30
Participant bias
- extending knowledge of placebo effect
- participants may intentionally or unintentionally bias their performance to align with the specific results expected of the experimental manipulation or cuz they want the experimenter to view them favourably
31
Blinding (blind experiment)
- when participants do not know whether they belong to the experimental or control group, or which treatment they r getting
- a method used to counter the placebo effect & participant bias --> while both groups of participants may be swayed by the placebo effect & their own biases, only the experimental group's performance is effected by the experimental manipulation
32
Experimental bias is
- Actions made by the experimenter, intentional or not, that influence the outcome of the experiment
- since experimenters know the hypothesis they're attempting to test, it's possible that they may promote the result they hope to achieve --> Ex, Eric may unintentionally spend more time encouraging his experimental group members to study more, hoping to find a strong effect of the drink
33
Double-blind studies
- experiments in which neither the experimenter nor the participants know which group each participant belongs to
- helps counter participant and experimental bias
34
statistics allow us to
summarize, interpret, and present the data that we have collected
35
Descriptive statistics
- present info about data at a glance to give u an overall idea of the results of the experiment --> organizes and sums up data
- includes the summary statistics like mean, median, & mode, & stdev (standard deviation)
- includes graphs like histograms
36
____________, ____________, & ____________, can be used to summarize useful & not so useful info that is communicated to the reader instantly
pie graphs, charts, & Venn diagrams
37
A histogram is
a type of graph used to report the # of times groups of values appear in a data set
- Horizontal axis is divided into groups of values called bins
- Vertical y axis measures the # of values in the dataset that fall into a given bin --> aka the frequency
38
Frequency distribution is
a type of graph illustrating the distribution of how frequently values appear in the data set
--> essentially a smooth curve that connects the peak of each bar in a histogram
- The range (diffe between the lowest & highest values) obtained= one measure of variability--> In some cases, typically those in which the mode is the preferred measure of central tendency, this may be the only useful measure of variability
- In other cases, where the data permit us to calculate means, the preferred measure of variability is the standard deviation
39
A normal distribution
- if we collect enough data for a given measure & graph the resulting distribution, it often looks smooth bell-shaped, symmetrical round a single peak = normal distribution
- aka bell curve
40
The measures of Central Tendency
- Descriptive statistical techniques for summarizing a distribution of data into a single value that represents the entire distribution.
- The 3 measures of central tendency are the mean, median, and mode. -->What makes them descriptive, is that they are based entirely on the available data.
- the measures of central tendency really only begin to describe a dataset cuz they only focus on the center of typical value alone --> really can't tell us how the other values fall around that point
41
Raw data
- Data collected from a study or experiment that has yet to be assessed using statistical data
--> any researcher can take the raw data & calc the same exact value for any measure of central tendency
42
Mean
- most common measure of central tendency --> cuz easy to understand & advanced statistical techniques depend on mean
- is calculated by adding together all the points in a data & then dividing them by the # of items in that set
- important to note that the mean is very susceptible to outliers
43
Outliers are
extreme points, distant from others in a data set
44
Mode is
- the value that appears most frequently in the set
- tells us the most typical response when looking at a dataset
- it's the only one that can be used for non-numerical datasets --> Ex, for polls on the best ice cream flavour
45
Median
- The center value in a data set when the set is arranged numerically (rank ordering)
- if even # of points, the mean of the 2 center points is used
- the advantage is it can't be swayed by an outlier
46
Variability is
the extent to which the scores in a data set tend to vary from each other & from the mean
47
measures of variability
- r a second group of descriptive statistics that review the spread of & distribution of a dataset
48
Standard deviation
- most common measure of variability
- is essentially a measure of the average distance of each data point from the mean
- datasets with larger standard deviation are more spread out than datasets with a smaller deviation
- this value gives us a picture of the variability of a set of data summarized into a single #
49
STANDARD DEVIATION: Low variability looks like what?
- group scores cluster around the mean
- narrow curve
- if its low variability & there's a 10 unit diff in means, the 2 frequency distribution may not overlap --> if you randomly selected one subject & looked at their score, you would know without a doubt which group they were in.
--> general rule is the lower the variability is, the more likely we r to attribute that diff to our IV manipulation
--> the simple version of the story is that the less overlap there is between the data from two groups (i.e., the less variability there is in the data), the less likely it is that we could have obtained the difference between group means by accident
50
STANDARD DEVIATION: High variability looks like what?
- group scores are much less tightly clustered around the mean
- wide curve
- if its high variability & there's a 10 unit diff in means, the 2 frequency distribution will probably overlap --> if you randomly selected one subject & looked at their score, you wouldn't really know which group they were in.
51
Inferential Statistics
- Statistics that allow us to use results from samples to make inferences about overall, underlying populations
- descriptive statistics are all based entirely on the data on hand, inferential statistics will require us to make some inferences about the nature of those data.
- Inferential statistics are a set of techniques developed to assess how likely it is that our sample data are an accurate reflection of the population as a whole.
- we need to look @ variability
52
INFERENTIAL STATISTICS: Essentially, u are hypothesizing that the experimental & control groups belong to diff populations. Explain
- The control groups belong to the general population & the experimental group belongs to a separate population under the positive effects of the energy drinks
- if Eric's hypothesis is correct, it would mean that the sample data he collects from the 2 groups is functionally drawn from 2 diff distributions.
--> if it's wrong, then all data is being drawn from 1 distribution (that of the general population)
53
it's straightforward to determine that 2 groups in an experiment r diff on some measured DV; we just use some descriptive statistics. The more important question__________________________________. there are 2 general possible explanations. Elaborate
The more important question is why those group means are different?
2 general possible explanations
1) (which we probably hope to be true) is that the group means r diff cuz of what we did (i.e., our manipulation of the IV).
2) this is the one that inferential statistics will actually evaluate--> is the only reason 2 groups r diff is that when we randomly selected and assigned groups, by chance we put all the people likely to score high on the DV in 1 group and all the people likely to score low on the DV in the other group.
54
T test
- A basic inferential statistic technique that considers each data point from both groups to calculate the probability that 2 samples were drawn from the same population
55
Purpose of T test
- even if 2 groups are both identically treated (no independent variable manipulation) there will still be some diff due to chance --> Eric can't tell fs if his results r from energy drink or chance
56
P-value
- A probability (0-1) indicating the likelihood of this diff being observed even if no "real" diff exists & these 2 groups acc belong to the same population
- T test produces a value which expresses this probability
- a p-value of less than 0.05 indicating that there is a less than 5% probability that they could've this diff between groups purely by chance --> scientists typically require a test to show a P-value of less than 0.05
57
Statistical significance
- When the diff between 2 groups is due to some true diff between the properties of the 2 groups & not simply due to random variation
- we look for a P-value of less than 0.05
58
State 2 types of statistical errors
- Type 1 error
- Type 2 error
59
Type 1 error
- aka false alarm
we conclude there is a diff when no diff actually exists in the real world
- Ex, when the boy calls wolf when there is no wolf
- Ex, claiming a drug is effective for a disorder when there was really no effect
60
Type 2 error
- aka a miss
- occurs when we fail to see a diff when a diff does exist
- Ex, when the boy fails to call wolf when there is one
- Ex, when an important scientific finding goes unreported, preventing further study into it
61
Type 1 & 2 Errors: Pregnancy Test
- TYPE 1: A biological man is told he is pregnant
- TYPE 2: A very evidently pregnant woman is told that she isn't pregnant
62
When do you use observational studies?
- sometimes it's impossible to conduct an experiment due to ethical or practical concerns --> Ex, studying link between smoking & cancer --> u can't have people start smoking
- in these cases u use observational studies
63
Observational Studies
- it's essentially observing the effect of variables they're interested in without performing any explicit manipulations
--> Ex, scientist studying harmful effects of smoking might collect data on cancer rates in existing smokers & non smokers, looking for any significant diff
- hypothesis can still be testing --> usually stated in the form of "what is the strength and direction of the relationship between x & y"
- data can still be analyzed using descriptive & inferential statistics
64
Correlation is
A measure of the strength and direction of the relationship between 2 variables
--> when scientist conducting an observational study finds that 2 variables are related, we say that the variables are correlated
--> when studying the relationship between 2 variables = correlation studies
- Correlational data are typically represented graphically using a scatter plot, plotting data as dots and drawing a line of best fit
65
How is the degree to which 2 variables are related measured?
- measured by the correlation coefficient = r
--> tells us both the strength & direction of the correlation
66
A correlation coefficient of 0 would indicate...
that there is no relationship whatsoever between the 2 variables
--> closer u get to +/- 1, the more correlated the 2 variables are, indicating a stronger relationship.
- All data points that fall perfectly along horizontal line --> r=0 cuz no increase or decrease in the independent value leads to any change in the dependent value
67
Correlation Coefficient (r) is
A number between -1 & +1, denoted by "r", indicating both the strength & direction of the correlation
- r= +1 --> variables are perfectly positively correlated --> Graph: As one variable increases other does too. Points fall in an almost perfect increasing linear line
- r= -1 --> variables are perfectly negatively correlated
--> Graph: As one variable increases, the other decreases. Points fall in an almost perfect decreasing linear line
- As relationship between 2 variables gets weaker, the correlation coefficient approaches 0
68
benefits of knowing the correlation between 2 variables
If 2 variables are correlated, knowing the value of one helps u predict the value of another
69
Are the slope of the line & r-value related?
- Nope!
- the trend line can be barely sloping upwards, but as long as the data points fall perfectly along it, the R-value will be +1
- same can be seen if the trend line slopes downward ever so slightly
70
Drawbacks to correlation studies
- correlation does not always equal causation
- they only allow u to observe the correlation between 2 variables --> reality is that any given variable we choose to observe may be influenced by any # of other variables, but the correlational approach only allows you to examine one pair at a time.
71
Correlation studies: the "third variable problem"
- While there is a surprising but clear relationship between 2 variables, it's probable that neither of these is the causal factor
--> much more likely that increased height & increased intelligence r the result of some ‘third variable’ that we have not included in our analysis. --> ex, childhood nutrition
72
Correlation ___________________ causation
DOES NOT EQUAL
73
Common Sense & intuition are not perfect: ob-ob mouse
- ob-ob mouse: mouse with genetic mutation associated with extreme obesity
- obvious answer to why they r fat is cuz they eat more food than normal mice --> it's tue but not quit the reason
- the mutation results in their bodies converting almost all the food they eat into fat, leaving little food energy for the body to actually run on. -->thus, the mouse needs to eat even more food, which also gets largely converted to fat, and so on.
- so acc the mouse isn't fat cuz it eats a lot, bit it eats a lot cuz it's fat
74
why is human perception unreliable?
- u have a strong tendency to perceive what u expect to perceive, even when ur expectations do not match up with reality.
- Your perception of the world is guided by prior experience, biases & heuristics.
75
Operational definition
- describes the actions or operations that will be made to objectively measure or control a variable
- key element in scientific study design -->in order to ask & ans questions, psychologists need to agree on exactly how the variables involved r defined.
-->ex, while "stress" has a conceptual definition as a feeling of strain, its operational definition might be "the score obtained on the Perceived Stress Scale"
2 important facts
a) operational definitions are essential
b) they are always open to argument
76
A construct is
a theoretical idea that is quite useful for describing a concept in a general way, but difficult to measure in practice.
--> ex, intelligence
77
Level of analysis
- diff perspectives that emphasize diff aspects of a research question
--> Common methods of analysis used in psychology include: learning, cognition, social, development, evolution, and neuroscience.
--> diff psychologists may look @ same situation but ask diff question
--> researchers ideally will apply a multi-level approach, incorporating ideas & findings from a variety of perspectives
78
Paradigms
A set of assumptions & ideas about what kind of research question can be asked & how they can be answered
--> researchers using diff levels of analysis are operating in diff paradigms (I think)
- variety of paradigms have been developed & discarded as discipline evolved
- there is competition among diff paradigms to provide the ‘best’ answers to important questions --> does not necessarily mean there's 1 ‘correct’ paradigm & all the others r wrong & useless. --> provide diff perspectives cuz people will only look for what they wanna find
79
There r 4 basic principles of scientific inquiry. List Them
1) Parsimony
2) Natural Order
3) Generalizability
4) Conservatism
80
1) Parsimony
- the principle of selecting the simplest explanation that adequately accounts for a phenomenon
- what makes an explanation ‘simple’ or ‘complex’ is the # of assumptions it makes
- this applies to the situation of competing explanations that do an equally good job of accounting for the known facts --> Of course, in many cases 2 competing theories will not be equally good at accounting for the known facts, and we don’t need to apply parsimony, we simply go with the better of the 2.
81
2) Natural Order
- as far as is possible, we will attribute the same effects to the same causes
--> drop a dish & it hits the kitchen floor -->attribute that to the same gravity that keeps the moon orbiting the earth
- in psych, we assume that a behaviour (ex, smiling) is a reflection of the same underlying mechanism in people all over the world. --> this case, root mechanism can be happiness
- important to realize that this principle is only to be applied in situations when comparing the same effects. --> display of teeth in animals may not indicate levels of happiness
82
3) generalizability
- the same causes that produce our effects in the lab also produce these effects in everyday life situations over which the scientist has no control
- it's the extent to which the findings of a study can be applied to a broader population, context, or time beyond the specific sample & conditions of that study (GOOGLE)
- many studies have been criticized for focusing heavily on a select demographic group in the studies; young, white, & middle to upper socioeconomic status, & then generalizing their conclusions to the entire human population --> has consequences for areas like social psych
83
4) Conservatism
- scientists r conservative in the sense that they tend to support the current explanation until new facts accumulate that the current explanation can't deal with
--> it's like driving the same care until it doesn't work, & then getting the best new care available
84
Empiricism is
the philosophical perspective that knowledge should be gained by direct observation by the world as it is, as opposed to rational perspectives that used logic & reason to determine how the world ought to be.
85
Inductive reasoning & theories
- move from a collection of specific observations (facts) to a theory that allows us to describe how these observations r related.
86
Deductive reasoning & theories
- used to test our theory by making specific predictions about situations or events that we have not yet observed directly.
--> and then testing them
87
Theory vs idea
What makes a scientific theory a scientific theory is that it generates testable predictions
--> Ex, predicting what will happened when u die is an idea not a theory cuz u can't test it
88
In order for a test to be considered useful for any sort of scientific inquiry, it must be both __________ and _______
reliable, valid
- there r many diff forms of validity, but we will focus on construction validity
89
Reliability
- The measurement consistency of a test (or of other kinds of measurement techniques)
- is the ability of any test to give the same output when the same input is entered
--> ex if u step on a weight scale multiple times in a short time period, the scale should give u the same #
--> ex, if u take a IQ test multiple times, u should get the same results
90
Construct Validity
- the extent to which there is evidence that a test measure a particular hypothetical construct
- refers to the ability of a test to measure what we intend to measure
91
A case study is
An in-depth investigation of an individual person or a small group of people, often over an extended period of time
- must ask urself if discoveries are person-specific or can they be generalized
- In areas where there may be no existing bodies of evidence to examine or theories to test and modify, case studies can provide us with some initial ‘facts’ to work with
- They can also serve to support (or not) existing theoretical positions.
- Almost everything we know about the human brain & its function has roots in case studies such as the one described above.
92
CASE STUDIES: So how do we move from observations of an individual to conclusions that we can safely apply to everyone
1) One possibility is to simply wait until more people show up in your office with injuries similar to those of your original patient --> this could take a very long time, & might never happen at all
2) Another possibility is you could go out into the world and actively search for such people.
93
Key diff between Experiments vs Case Studies & Correlation Research
- unlike the other methods where data is collected from the world as it is, in experiments, a researcher manipulates 1 or more variable (i.e., they makes something happen) & measure the changes in a 2nd variable
94
Why do we need both experimental & non-experimental research tools (like case & experimental studies)
- Without data from the real world, our experiments would lack direction & relevance.
- Without experimental data, our understanding of the world would lack clear cause & effect explanations.
95
Case Study: Advantages & disadvantages
A- Lots of in-depth information; can be helpful to provide direct evidence of a theory particularly when studying an unusual phenomena.
d- Not generalizable to the population; can be subjective if researcher expects to find support for a specific theory.
96
Direct Observation: Advantages & disadvantages
A- If done in natural environment there is reduced artificiality compared to research lab setting
- can allow for long periods of data collection rather than a snapshot observation or measure of behaviour --> useful technique when little is known about subject or phenomenon of interest.
D- Often observers cannot avoid being noticed or being intrusive which could bias response of phenomena under study; difficult to explain rationale of behaviours observed; observers may be biased or have subjective interpretations of what is observed.
97
Experiment: Advantages & disadvantages
A- Researcher has strict control over manipulation of variables and setting; allows for high accuracy in drawing conclusions of cause and effect relationships.
d- Setting can be artificial and results may not translate to those found in natural settings; due to ethical and moral constraints many experiments cannot be conducted in this environment.
98
Interviews: Advantages & disadvantages
A- Often conducted one-on-one which allows interviewer to ask follow up questions for clarification and assess the honesty of the interviewee; can gather information on behaviours that may otherwise be difficult to observe.
d- Interviewee may not be comfortable answering some questions or may be either unintentionally or intentionally dishonest due to social desirability bias, memory lapses.
99
Questionnaire : Advantages & disadvantages
A- Can gather information on behaviour that might otherwise be difficult to measure or observe; usually relatively simple to collect data from large samples; allows for collection of self-report or observations by someone other than the researcher.
d- Difficult to assess truthfulness of self-report data due to social desirability bias, memory lapses, wishful thinking, response set; participant may not finish all questions rendering data inadmissible & difficult, if not impossible, to draw conclusions.
100
Replication is
the replication of a study to see whether the earlier results can be duplicated, often times by the independent researchers
- any new finding or surprising result will be regarded with some degree of skepticism until such time as other independent researchers have conducted similar studies & obtained similar results
- where a Type I or Type II error occurs, repeating the experiment with a diff sample shouldn't produce same results, alerting us to the possibility that one of these errors occurred in the og study.
101
Questionnaires
A research tool in which a participant responds to a written list of items or questions
102
Interviews
A research tool during which the investigator asks the participant questions, often these may be structured or semi-structured in nature
103
The Behaviourists quite correctly argued that any data produced by asking people to _____________________________ _____________________________ are subject to ___________ & _________.
report their personal experience, such as questionnaires, interviews, & personal inventories
distortion & errors
104
Social desirability bias is
a tendency to give socially approved ans to questions about oneself
- This bias may lead people to answer questions in ways they think will make them look good, rather than answering with complete honesty
105
Response sets
A tendency of research participants to respond to questions in a particular way that is unrelated to the content of the questions
- a tendency to respond to questions in a particular way regardless of the content (e.g., a person who answers ‘C’ to every question on a multiple choice test)
--> subject may misunderstand an ambiguously worded question, incorrectly recall personal information, or become bored with a repetitive task or series of questions
- Researchers often include control scales in surveys and questionnaires, which r designed to detect participants who are answering in some systematic, untruthful way --> it's impossible to control for all such errors.
- researchers often measure more objectively observable DVs along with self-report measures --> the self-report data may contain information that we would never learn by examining physiological variables alone.
106
The famous horse
- was able to solve math problems by stomping his foot
- even when his trainer wasn't there but audience was
- however he couldn't when he was placed in a space with no audience or trainer
- researcher realized that he read the facial cues of people like his trainer & audience --> when he was just one off, the audience and trainer would instinctively lean in unintentionally, which the horse picked up on
107
SLIDESHOW: #5 conclusions about MacIntroPsych
#1) Some of the things u know to be
true about psychology r wrong.
#2) Perspectives in psychology change the focus of research questions we pursue & the type of ans we find.
#3) Psychological processes r
embedded in biology.
#4) ur perception of the world is
guided by prior experience, biases
& heuristics.
#5) We only have access to a
limited subset of reality.
Psych 1X03
flashcards
Decks in class (13)
# Cards
-
Research methods 1107
-
Textbook: Intro: foundations of psych11
-
Classical Conditioning82
-
Instumental conditioning69
-
Problem Solving & Intelligence65
-
Language82
-
Categories & Concepts57
-
Science of learning22
-
chapter 7 textbook: Attention0
-
Attention!73
-
Influence of others oney158
-
Forming Impressions (HEHE)69
-
Memoryyyyyyyyyyyyy106
