10. applied psychology - university of...

10. Applied Psychology

1

• Understand how psychological research can be taken beyond the lab

• Understand the difficulties with control in applied research, and their (partial) solutions

• Identify the additional difficulties with doing ethical research outside the lab

• Know the various forms of quasi-experimental designs that are typically used in applied research

• Know the basic components of program evaluation

Goals

2

Applied Psychological Research

Examples:• What’s the best way to teach kids to read?• What can we do to prevent drug addiction?• How can we make workers more productive?• How can we design a computer program to make it

more intuitive?• What characteristics of a community lead its members to

have high quality of life?

Research aimed at solving some particular practical problem

3

• Clinical Ψ: Treating & managing mental illness

• Educational Ψ: Improving teaching & schools

• Health Ψ: Harnessing positive Ψ effects on physical illness and reducing harmful ones

• Sports Ψ: Improving athlete performance

• Human Factors Ψ: Improving design of tools/systems so they are intuitive and helpful

• Community Ψ: Improving community life.

A Few Areas of Applied Psychological Research

4

A Few Applied Research Tasks For Clinicians

• Understand and critically analyze studies to improve one’s own practice

• Evaluate old intervention programs (how well do they work?)

• Plan and evaluate new intervention programs (how well will they work?)

• Educate co-workers about Research Methods

• Complete program surveys

5

Ethics in Applied Psychological Research

6

Ethics in Applied Research

• Can involve all the ethical difficulties of field research and more

• Applied research examines how people get paid, graded, certified, medically treated, served by government agencies, etc. etc.

• i.e., it hits people where they live, in a way that basic field research does not

• Plus, it often involves recommendations for change, which may be unwelcome

7

Ethics in Applied Research

• Applied research also frequently involves vulnerable and/or marginalized populations (disadvantaged children, the sick, the homeless, prisoners, prostitutes, etc.)

• In extreme cases, it may even have the potential to endanger your participants (e.g., victims of abuse) or the researchers

• Therefore, more care must be exercised in general with all aspects of research ethics

8

• In institutional / industrial settings, must take extra steps to avoid (perception of) coercion Example: Emphasizing to prisoners that (non) participation will not be noted in their record

• Rewards for participation must not be out of line with what people normally receive in that setting Example: Offering disadvantaged school students 100$ for participation may be seen as coercive

Ethics in Applied Work: Voluntary Participation

9

• In field settings, must take extra steps to emphasize freedom to discontinue Example: Emphasizing to students that quitting in the middle of a reading study is allowed and they will not be penalized (i.e., it’s not a test!)

• Also, may have to consider peer pressure Example: Athletes in a sports psych study may feel that they cannot quit because this would demonstrate a lack of “team spirit”

Ethics in Applied Work:Freedom to Discontinue

10

• As with any study, if participants are children you must obtain consent from guardians and assent from children

• Deception is generally to be avoided in field work, esp. with vulnerable or captive persons

• Full debriefing may be difficult in field research, but should be attempted

Ethics in Applied Work: Informed Consent

11

• Confidentiality may be more difficult to protect in field settings

• Extra care should be taken when considering stressful or lengthy testing with disadvantaged groups (may not be practical in any case)

• Control group participants may demand to be in test group because they perceive it as better (tied to design issue: blinding is usually not possible)

Ethics in Applied Work: Other Issues

12

• Stakeholders in the research may have biases in terms of how they would like the research to turn out.

• This can lead to pressure to fabricate/misrepresent data, suppress publication, or engage in non-disclosure of authors or financial supporters

• One guard against this is to have an a priori agreement as to how the data will be analyzed and reported, regardless of outcome (see Box 10.1 in text).

Ethics in Applied Work: Scientific Misconduct

13

Scientific Misconduct

14

Discussion / Questions

• Name some applied research tasks for clinical psychologists

• What does consent mean? What does assent mean?

15

Design Issues in Applied Psychological Research

16

• Applied work generally has high external validity, because it takes place in real-world settings

• But this comes at the cost of low internal validity: - Random assignment may not be practical - Matching may lead to regression problems - Counterbalancing may not be possible - Blinding / Double-blinding may fail - Etc. etc...

• Great care must be taken to design applied studies so that they will have interpretable results

17

Design Issues in Applied Research

• True experiments have manipulated IVs, measured DVs and all else held constant.

• When this is not possible, one may instead use a quasi-experimental design: Non-equivalent groups designs PxE factorial designs Non-equivalent Control Group designs Interrupted Time Series designs

Quasi-Experiments

18

✓✓

• Used when random assignment not possible

• Control group is selected from different population than treatment group

• Built-in non-equivalent groups confound, but better than no control group at all

Nonequivalent Control Group Designs

19

• Does watching Jersey Shore lower IQ?

• Let’s say a Jersey Shore marathon is coming up and you suspect that it will kill braincells in those exposed to it, thus lowering IQ

• You could just measure viewer IQ pre/post, but any differences might be due to history/maturation confounds

Example

20

• So you decide to use a control group of people who are not going to watch it

• Practical problem: You can’t randomly assign people to watch the marathon or not*

• Instead, might use a convenience sample of people who plan to not watch the show

Example

21

*Also, it would be ethically wrong to force Snooki on the unwilling

• Built-in non-equivalent groups problem

• Partial solution: Look at difference in scores between pre- and post-test for both groups

• Jersey Shore viewers should show greater IQ losses pre/post than non-viewers

• But interpretation can still be problematic...

Example Nonequivalent Control Group Design

22

23

!"#

$"#

%""#

%%"#

%&"#

'()*+(# ,-(+#

!"#

.(+/(0#12*+(# 3*#.(+/(0#12*+(#

!"#

$"#

%""#

%%"#

%&"#

'()*+(# ,-(+#

!"#

.(+/(0#12*+(# 3*#.(+/(0#12*+(#

!"#

$"#

%""#

%%"#

%&"#

'()*+(# ,-(+#

!"#

.(+/(0#12*+(# 3*#.(+/(0#12*+(#

!"#

$"#

%""#

%%"#

%&"#

'()*+(# ,-(+#!"#

.(+/(0#12*+(# 3*#.(+/(0#12*+(#

Some evidence of IQ loss, but could be a floor effect or downward regression

No evidence of IQ loss relative to controls

Good evidence of IQ loss, but could still be ceiling effect

Best evidence of IQ loss, but could still be a “history x group interaction confound” (true in all cases with this design)

• Other ways to partially solve nonequivalent group problem:

• Measure possible confounding variables pre- and post-test, to see if they can provide alternative explanation of results.

• Select sub-groups that match on important variables from the two populations (Beware regression!)

Example Nonequivalent Control Group Design

24

• Matching can cause problems with regression to the mean in non-equivalent (control) groups designs.

• Example:

• Group A has a lower population mean on the matching variable than Group B

• Therefore, to create matching pairs of subjects, have to pick high scorers in Group A and low scorers in Group B

• These extreme scorers then regress to their population means, causing a confound.

Regression to the Mean and Matching

25

• Asked to evaluate an existing program designed to increase optimism in people with depression

• But, hospital refuses to halt this treatment to randomly-chosen half of its patients, because they believe it is beneficial

• Therefore, must use non-equivalent control group. Say, a group of non-depressed persons from the general population.

• Problem: Non-depressed persons will tend to have higher starting optimism scores

• Solution (Or is it???): Match on initial level of optimism.

Example of Regression & Matching Problem

26

• Problem: To match, you’d have to pick higher-than-average depressed people and lower-than-average non-depressed people

• These extreme samples’ scores will tend to regress to their respective populations’ means over time

• Non-dep sample will go up in optimism towards their population mean

• Dep sample will go down in optimism, towards their population mean

Example of Regression & Matching Problem

27

0

5

10

15

20

25

30

Depressed Non-depressed

Opt

imis

m

=

Before regression, matched samples from non-equivalent populations are equal...

28

0

5

10

15

20

25

30

Depressed Non-depressed

Opt

imis

m

<

On second measure, after regression, matched samples from non-equivalent populations are not equal!

29


• What is a selection x history confound?

30

Why RttM Happens• You measure a large group of people on variable X and find their scores

are normally distributed, with a mean of 20.

• You then select out the people with the top 50%* of the scores. Let’s say the mean of the higher-scorers is 30.

• You then measure these high-scorers a second time. Will their mean be 30 again?

• No. What you will find is that that the mean of the second set of scores will be between 20 and 30, closer to the mean of the overall normally-distributed group of scores.

• Why does this happen? Let’s consider a couple of extreme hypothetical scenarios...

* Note that the same would happen if you took the lower 50% of scores or the upper 10% or whatever. The key is some kind of biased selection.

31

The “No RttM” Scenario

• Imagine I measure all your heights using a perfect measuring instrument that always gets your height exactly right, with no error whatsoever. Let’s say the class average is 170 cm.

• Now I take the people with the 10 highest measurements. Let’s say their mean is 180 cm.

• If I measure these high-scorers a second time, I will find that their mean is again 180 cm (indeed, each of their individual measures will be exactly the same by definition).

32

The “Complete RttM” Scenario

• But now imagine I instead measure all your heights using a completely inaccurate measuring instrument that just returns normally-distributed random values between with a mean of 170 cm. Again, the class’s mean would be around 170 cm.

• Now I select out the people with the 10 highest measurements. Let’s say their mean is again 180 cm.

• If I measure these high-scorers a second time, I will find that their mean is very near 170 cm (indeed, their second individual measures will be completely unrelated to their first measures by definition).

33

The “Some RttM” Scenario

• Finally, let’s say I measure you all with a real ruler, that is not perfect, but is also not completely random. Let’s say again I get a mean of around 170 cm.

• Again, I select out the 10 high scorers. Let’s again assume that their mean on first measure is 180 cm.

• If I measure these high-scorers a second time with my not-perfect-but-not-random ruler, what mean will I get? It won’t be 170 (like with the random ruler) and it won’t be 180 (like with the perfect ruler), but will be somewhere in between. Where exactly depends on how close to perfect my ruler is.

34

Why this Won’t Hurt You if you Measure the Whole Population• Consider the case where I measure the whole group several

times:

• Using the perfect ruler, I will get a mean of 170 each time (with no variability from one measure to the next).

• Using the random ruler, I will tend to get a mean of around 170 cm each time (with large variability).

• Using the real ruler, I will tend to get a mean of around 170 cm each time (with small variability).

• The key ingredient in producing regression to the mean is the biased selection of high or low scores

35

Why This Won’t Hurt You If You Use Random Sampling

• Consider the case where I measure a random, unbiased sample of the group several times:

• Using the perfect ruler, I will tend to get a mean of 170 each time (with very little variability).

• Using the random ruler, I will tend to get a mean of around 170 cm each time (with very large variability).

• Using the real ruler, I will tend to get a mean of around 170 cm each time (with moderate variability).

• Again, the key ingredient in producing regression to the mean is the biased selection of high or low scores

36

RttM is Due to Measurement Error

• When measuring, you never get the true score

• Rather, you get the true score ± measurement error

• Error is normally distributed, so you usually get close to the true score, but sometimes you get well below or well above it.

True Score

Normal distribution of measurement error

37


• When categorizing based on the measured score, you will sometimes miscategorize due to measurement error

• Individuals like the one at right belong in the high scorers, but may end up in the low scorers True Score


Group Median

HighScorers

LowScorers

38


• Such “miscategorized” individuals will likely be closer to their true score the next time you measure

• This brings the low scorers’ mean up and the high scorers’ mean down

True Score


Group Median

HighScorers

LowScorers

39


• Another way to think of this is that when you select the high scorers, you are in part selecting some “upward error” along with some “upward true scores”.

• When you measure a second time, the error is symmetrical--equal upward and downward errors--so the mean of the high score group drops relative to the first measure.

40

Why This WILL Hurt You If You Use A Median Split

• Consider the case where I measure the IQ of everybody in the class. Let’s say the average is 120.

• I then divide the class into “high” and “low” groups via a median split. Let’s say the high group mean is 130 and the low group mean is 110.

• What happens when I measure them again? The low group will rise to something between 110 and 120. The high group will fall to something between 130 and 120.

• Imagine how this would confound attempts to study effects of some other variable on IQ!

43

So Why Then Is the Median Split Used So Often?

• Despite what we’ve learned about regression to the mean and median splits, this technique is used very frequently in the literature

• How is this justified? Well, the degree of RttM is related to the degree of measurement error.

• So if the effect of your treatment is large relative to your degree measurement error, the treatment effect will overcome the RttM effect.

• But RttM always has some degree of confounding effect in median split studies

44

So Why Then Is the Median Split Used So Often?

• Short answer: It shouldn’t be.

• Dichotomizing a continuous variable:

• Leads to regression to the mean confounds (as we’ve just seen)

• Reduces statistical power

• Messes with individual differences

• Don’t do this. Keep the data continuous and use correlation/multiple regression methods for analysis

MacCallum, R.C., Zhang, S., Preacher, K.J., & Rucker, D.D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19-40.

45

Median Split Reduces Statistical Power

46

Effects on Individual Differences Of

Dichotomization

47

Extreme Scores

• One way people talk about RttM is that an extreme score tends to be followed by a more moderate one

• This is because the more extreme a score is, the more likely it is to be due chance or measurement error

• Example: Athletes who have excellent rookie years will usually follow this with a “sophomore slump”

48


• Is your mind blown yet?

49

Regression to the Mean: Implications

• What does this all mean in terms of research? Two things:

• Avoid splitting groups into high/low scores like this*

• If you take a sample that is biased towards high scores, its mean will drop on 2nd measure**

• If you take a sample that is biased towards low scores, its mean will rise on 2nd measure**

* No, it doesn’t help to take just the upper quarter and lower quarter or anything like that... ** Assuming no other effects, that is.

50

!"#

$"#

%"#

&"#

'"#

(""#

)*+# ),-.#

!"#

$%&$'

/+0*+--+1# 2,341+0*+--+1#

Regression effect downwards (depressed)

Regression effect upwards (controls)

Effects of Regression on Interpretation

If you have used biased sampling to equate your non-equivalent groups, and your therapy is ineffective, this is the pattern you will see...

Your therapy will seem to actually be hurting the participants!

51

!"#

$"#

%"#

&"#

'"#

(""#

)*+# ),-.#

!"#

$%&$'

/+0*+--+1# 2,341+0*+--+1#



If Moderate Therapeutic Effect...

Therapeutic effect upwards (depressed)

If you have used biased sampling to equate your non-equivalent groups, and your therapy is moderately effective, this is the pattern you will see...

Again, your therapy will seem to actually be hurting the participants (relative to the controls).

52

!"#

$"#

%"#

&"#

'"#

(""#

)*+# ),-.#

!"#

$%&$'

/+0*+--+1# 2,341+0*+--+1#

Even If There’s a Strong Therapeutic Effect

Big therapeutic effect upward (depressed)



If you have used biased sampling to equate your non-equivalent groups, then even if your therapy is high effective, this is the pattern you will see...

Your therapy doesn’t seem to be doing any good (relative to controls)

53

Non-Equivalent Control Group: Summary

• Use an equivalent control group where possible

• If non-equivalent control group is all you can get, use it

• Do measure the groups on potential confounding variables

• Don’t try to match groups from populations that differ

54

• Some non-equivalent groups quasi-experiments do not use a pre-test.

• This severely weakens interpretability, but is sometimes necessary for practical reasons.

• Common in studies of effects of major unpredictable historical events

• Interpretation in this case must be based in part on prior knowledge of the phenomenon under study

Non-equivalent Groups Design, No Pre-Test

55


56

• A method for evaluating effects of an event (planned or anticipated) in a broader temporal context

• Many measures taken before event and many taken after.

• Helps to detect trends that provide a better understanding of effects of an event.

Interrupted Time-Series Designs

57

• Let’s say a safe-sex awareness program was briefly implemented at UO in Summer 2009, with the goal of reducing unwanted pregnancy, STDs, etc.

• However, let’s say the program was shut down after just 3 months due to lack of funding

• A retrospective, archival ITS design might be a good way to see if the program was successful, and if it thus deserves to be re-established

Interrupted Time-Series Design: Example

58

!"

#"

$!"

$#"

%!"

%#"

&!"

&#"

'!"

'#"

#!"

()**+,"%!!-" ./00"%!!-"

123/24+5"6,+7"

(89:"

Data Examined in IsolationHere we see a simple pre-post examination of two variables of interest. Based on this it looks like the program worked, but this is very weak evidence. Who’s to say this drop would not have occurred without the program?

Stan

dard

ized

Rat

e

59

!"#"

$!"$#"%!"%#"&!"&#"'!"'#"#!"

()*+,

-"%!!."

(/001*"%!!."

2344"%!!."

5+,61*"%

!!7"

()*+,

-"%!!7"

(/001*"%!!7"

2344"%!!7"

5+,61*"%

!$!"

()*+,

-"%!$!"

(/001*"%!$!"

2344"%!$!"

5+,61*"%

!$$"

()*+,

-"%!$$"

(/001*"%!$$"

8,93,61:";*1-"

(<=>"

Data Examined in Historical Context

By examining our data in a larger historical context, we get a better idea of what they mean. Here we have much better evidence that the program worked, although interpretation is still subjective (statistical analysis would help).

Stan

dard

ized

Rat

e

60

!"

#!"

$!"

%!"

&!"

'!"

(!"

)*+,-

."$!!/"

)0112+"$!!/"

3455"$!!/"

6,-72+"$

!!8"

)*+,-

."$!!8"

)0112+"$!!8"

3455"$!!8"

6,-72+"$

!#!"

)*+,-

."$!#!"

)0112+"$!#!"

3455"$!#!"

6,-72+"$

!##"

)*+,-

."$!##"

)0112+"$!##"

9-:4-72;"<+2."

)=>?"


Examining data in a larger historical context also provides for more interpretable falsifying cases. Here we see evidence that the drop in measured variables is likely just part of a larger historical trend, and may have nothing to do with the program under study.

Stan

dard

ized

Rat

e

61

!"#"

$!"$#"%!"%#"&!"&#"'!"'#"#!"

()*+,

-"%!!."

(/001*"%!!."

2344"%!!."

5+,61*"%

!!7"

()*+,

-"%!!7"

(/001*"%!!7"

2344"%!!7"

5+,61*"%

!$!"

()*+,

-"%!$!"

(/001*"%!$!"

2344"%!$!"

5+,61*"%

!$$"

()*+,

-"%!$$"

(/001*"%!$$"

8,93,61:";*1-"

(<=>"


Some data is cyclical and such cycles must be taken into account when analyzing time series results. This pattern would suggest that the program had no effect and that the drop was simply part of regular cyclical change in behaviour.

S

tand

ardi

zed

Rat

e

62

Variations on ITSDs

• Interrupted time series with nonequivalent control group

• Interrupted time series with switching replications

• Multivariate interrupted time series

63

VariationsInterrupted time series with nonequivalent control group

64

!"

#"

$!"

$#"

%!"

%#"

&!"

&#"

'()*+,"%!!-"

'.//

0)"%!!-"

1233"%!!-"

4*+50)"%!!6"

'()*+,"%!!6"

'.//

0)"%!!6"

1233"%!!6"

4*+50)"%!$!"

'()*+,"%!$!"

'.//

0)"%!$!"

1233"%!$!"

4*+50)"%!$$"

'()*+,"%!$$"

'.//

0)"%!$$"

7892:2"

;2)3058+"

U

nw

an

ted

Pre

gnan

cie

s

VariationsInterrupted time series with switching replications

66

!"

#"

$!"

$#"

%!"

%#"

&!"

&#"

'()*+,"%!!-"

'.//

0)"%!!-"

1233"%!!-"

4*+50)"%!!6"

'()*+,"%!!6"

'.//

0)"%!!6"

1233"%!!6"

4*+50)"%!$!"

'()*+,"%!$!"

'.//

0)"%!$!"

1233"%!$!"

4*+50)"%!$$"

'()*+,"%!$$"

'.//

0)"%!$$"

7892:2"

;2)3058+"

<.00+="Un

wan

ted

Pre

gnan

cie

s U

nw

an

ted

Pre

gnan

cie

s


• What is the advantage of a switching replications design over a basic ITS design?

• How does gathering multiple variables in an ITS enhance confidence in interpretation?

67

Program Evaluation(You’d better get loaded for bear)

68

• Research applied to assessing any complex coordinated activity aimed at a particular goal Examples: government programs, school depts., community projects, therapy groups, etc.

• Asks questions such as:

• Is there a need for the program?

• Is the program being run as intended?

• Did the program do what it was supposed to?

• Were the results of the program worth the costs?

Program Evaluation

69

• Campbell: People should take an experimental approach to social reforms, such as govt program, laws, etc.

• Attempts to assess reforms can be hampered by political ideology and self-interest. Scientific approach mitigates this.

• “Trapped Administrators” start with an ideology (“Just say no”) and try to find information to support it.

• “Experimental Administrators” start with a goal (reduce the harm drugs cause), are willing to assess various ways to accomplish it, and are willing to abandon ways that are shown empirically to be ineffective

Reforms as Experiments

70

• Is there a need for the program? How often will it get used?

• Different from a demand for the program (public vulnerable to availability heuristic, etc.)

• Careful of tendency for needs assessment to be biased towards what is already deliverable by existing systems

• As size/cost of program increases, necessity of detailed needs analysis increases.

Does Springfield really need a bear patrol?

Needs Analysis

71

• Archival data

• Surveys of available resources

• Surveys of stakeholders

• Key informants

Sources of Information for Needs Assessment

72

• Sources of archival data: Census data, newspapers, meeting minutes, weather reports, blog entries, etc. etc. etc.

• How many bear encounters have there been in Springfield (animal control records, hospital records, police reports, etc)?


73

• Survey of available resources: What exists to deal with this problem already?

• Government services, NGOs, private companies, neighbourhood groups, etc.

• Talk to Springfield’s animal control service (if they have one) and police to see if they are able to handle the occasional bear problem.


74

• Surveys of potential users and other stakeholders

• Stakeholders: Anyone affected by a program, including those who will use it, run it, or be affected by it (community members, etc.)

• Can assess how many people will use a program

• How many people want a bear patrol? What if it will cost a few more dollars in municipal taxes?


75

• Key informants: High-level public servants, clergy, neighbourhood association leaders, etc.

• Must be sensitive to potential biases

• Focus group: Small group that discusses the need for a particular program in an open-ended fashion

• Can be used as follow-up to survey, or as pre-cursor to determine what q’s should be asked.

• Community forum: Meeting with all stakeholders invited.

• Can attract extremes of opinion


76

• Is the program being run as it should?

• How often are people using the program? Are they aware of it?

• Are procedures being followed? Are necessary resources being obtained?

• May involve regular Program Audits.

Does the bear patrol have enough bombers & trucks? Do people know what number to call?

Formative Evaluation

78

• Before starting any major program, a smaller version can be implemented

• This can be part of a formative evaluation, to determine if the program will be effective, what flaws it might have, what it will really cost, etc.

Before buying a bunch of stealth bombers and trucks, maybe the people of Springfield should have hired one guy with a tranq gun and a pick-up and see if that does it...

Pilot Studies

79

• Is the program useful? Did it accomplish its goals?

• Can be politically very sensitive to even suggest one be done

• Usually quasi-experimental, but equivalent waiting-list control group is sometimes available when services outstrip demand.

Summative Evaluation

80

• Assessing null results can be problematic because “Failing to reject the null” does not imply “accepting the null”

• Worse, the quasi-experimental nature of the findings often leaves room for politically-biased interpretation

• However, replicated null results can and should lead to program cancellation or at least modification


81

• If bear encounters in Springfield drop from 1 to 0, does that justify the program?

• One could characterize it as “A 100% drop! A complete eradication of the bear scourge!”

• Or one can see it as a negligible problem experiencing a negligible drop

Might the bear encounters have dropped to zero without the bear patrol, was the program just an “elephant-repelling rock”?


82

• Is the program worth the money?

• If one program costs twice as much as another, but produces only 10% improvement, is it worth it?

• Requires attaching dollar values where this may be difficult (e.g., human lives, suffering)

Considering the immense cost of the bear patrol (One stealth bomber costs a B$!), and the limited results (bear encounters drop from one to zero)...

Cost/Benefit Analysis

83

Understanding Programs

• Program evaluation is more than just figuring out if a program works.

• It is also figuring how the parts of the program are supposed to work together in order to produce desired outcomes

• This involves generating a logic model.

• This describes a program’s inputs, activities, outputs and outcomes

84

Example Logic Model

CCM = Chronic Care ModelHBP = High Blood Pressure

85

• Type of analysis used in many types of studies--case studies, ethnographies, grounded theory studies, etc.

• In contrast to quantitative:

• Samples not representative of large pop., but rather focussed: Ss chosen for particular characteristics.

• Experimenter not “removed from the equation”, but rather stays in the situation being studied (and must spell out POV, biases, role, etc.)

• Typically aimed at generating or elaborating on hypotheses rather than testing them

Qualitative Analyses(see CMN 3103)

86

• Data comes in many forms: Interviews, archival photos, personal artifacts, diaries, etc. etc.

• Data gathering and analysis are “open ended”, researcher goes where the data leads him/her.

• Analysis is more intuitive. Researcher looks for themes and motifs in the data and links between them. Similar to literary analysis in some ways.

Qualitative Analyses(see CMN 3103)

87

• Data and theoretical details can lead away from larger questions of interest

• Introspection, discussion, and naturalistic observation can be good ways to keep yourself “grounded”

• Combinations of methodologies examining applied and basic questions are often most informative.

Research in the Real World: Some Thoughts

88


• What do you think of this program? That is, the undergraduate program in psychology?

89

10. applied psychology - university of...

Documents