Presentation is loading. Please wait.

Presentation is loading. Please wait.

Non-Experimental designs: Developmental and Small-N Designs

Similar presentations


Presentation on theme: "Non-Experimental designs: Developmental and Small-N Designs"— Presentation transcript:

1 Non-Experimental designs: Developmental and Small-N Designs
Psych 231: Research Methods in Psychology

2 Reminders Don’t forget Quiz 9 is due on Friday at midnight
Post Exam 2 extra credit – posted on ReggieNet, due in-class on Wednesday Journal Summary #2 Due in labs this week Reminders

3 Non-Experimental designs
Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each Non-Experimental designs

4 Developmental designs
Used to study changes in behavior that occur as a function of age changes Age typically serves as a quasi-independent variable Three major types Cross-sectional Longitudinal Cohort-sequential Developmental designs Video lecture (~10 mins)

5 Developmental designs
Cross-sectional design Groups are pre-defined on the basis of a pre-existing variable Study groups of individuals of different ages at the same time Use age to assign participants to group Age is subject variable treated as a between-subjects variable Age 4 Age 7 Age 11 Developmental designs Examples:

6 Developmental designs
Cross-sectional design Advantages: Can gather data about different groups (i.e., ages) at the same time (a “snapshot” of a population at a particular time) Participants are not required to commit for an extended period of time Developmental designs

7 Developmental designs
Cross-sectional design Developmental designs

8 Developmental designs
Longitudinal design Follow the same individual or group over time Age is treated as a within-subjects variable Rather than comparing groups, the same individuals are compared to themselves at different times Changes in dependent variable likely to reflect changes due to aging process Changes in performance are compared on an individual basis and overall Age 11 time Age 20 Age 15 Developmental designs

9 Longitudinal Designs Examples Wisconsin Longitudinal Study (WLS)
Began in 1957 and is still on-going (~60 years) 10,317 men and women who graduated from Wisconsin high schools in 1957 Originally studied plans for college after graduation Now it can be used as a test of aging and maturation Data collected in: 1957, 1964, 1975, 1992, 2004, 2011 Physicians’ Health Study Nurses’ Health Study Longitudinal Designs

10 Developmental designs
Longitudinal design Advantages: Can see developmental changes clearly Can measure differences within individuals Avoid some cohort effects (participants are all from same generation, so changes are more likely to be due to aging) Developmental designs

11 Developmental designs
Longitudinal design Baby boomers Generation X Mellennials Generation Z Disadvantages Can be very time-consuming Can have cross-generational effects: Conclusions based on members of one generation may not apply to other generations Numerous threats to internal validity: Attrition/mortality History Practice effects Improved performance over multiple tests may be due to practice taking the test Cannot determine causality Developmental designs

12 Developmental designs
Cohort-sequential design Measure groups of participants as they age Example: measure a group of 5 year olds, then the same group 10 years later, as well as another group of 5 year olds Age is both between and within subjects variable Combines elements of cross-sectional and longitudinal designs Addresses some of the concerns raised by other designs For example, allows to evaluate the contribution of cohort effects Developmental designs

13 Developmental designs
Cohort-sequential design Time of measurement Cross-sectional component 1975 1985 1995 Age 5 Age 15 Age 25 Cohort A 1970s Age 5 Age 15 Cohort B 1980s Age 5 Cohort C 1990s Longitudinal component Developmental designs

14 Developmental designs
Cohort-sequential design Advantages: Get more information Can track developmental changes to individuals Can compare different ages at a single time Can measure generation effect Disadvantages: Still time-consuming Need lots of groups of participants Still cannot make causal claims about age variable Developmental designs

15 Non-Experimental designs
Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each Non-Experimental designs

16 Small N designs What are they?
In contrast to Large N-designs (comparing aggregated performance of large groups of participants) One or a few participants Data are typically not analyzed statistically; rather rely on visual interpretation of the data Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes Small N designs are common place in some areas of psych. e.g., applied behavioral analysis, psychophysics, clinical settings, animal studies, expertise, etc. Small N designs

17 Small N designs Small vs. Large N debate
Some researchers have argued that Small N designs are the best way to go. The goal of psychology is to describe behavior of an individual Looking at data collapsed over groups “looks” in the wrong place Need to look at the data at the level of the individual Smith and Little (2018). Small is beautiful: In defense of the small-N design, Psychonomic Bulletin & Review (online March) Small N designs

18 Small N designs Baseline experiments – the basic idea is to show:
= observation Treatment introduced Steady state (baseline) Baseline experiments – the basic idea is to show: Observations begin in the absence of treatment (BASELINE) Essentially our control/comparison level Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded Small N designs

19 Small N designs Baseline experiments – the basic idea is to show:
= observation Reversibility Treatment removed Transition steady state Steady state (baseline) Treatment introduced Baseline experiments – the basic idea is to show: When the IV occurs, you get the effect When the IV doesn’t occur, you don’t get the effect (reversibility) This allows other comparisons, to the original baseline as well as to the transition steady state Small N designs

20 Unstable Stable Before introducing treatment (IV), baseline needs to be stable Measure level and trend Level – how frequent (how intense) is behavior? Are all the data points high or low? Trend – does behavior seem to increase (or decrease) Are data points “flat” or on a slope? Small N designs

21 ABA design ABA design (baseline, treatment, baseline)
Steady state (baseline) Transition steady state Reversibility ABA design (baseline, treatment, baseline) The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e.g., history, maturation, etc.) There are other designs as well (e.g., ABAB see figure14.6 in your textbook) ABA design

22 Small N designs Advantages
Focus on individual performance, not fooled by group averaging effects Focus is on big effects (small effects typically can’t be seen without using large groups) Avoid some ethical problems – e.g., with non-treatments Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices) Often used to supplement large N studies, with more observations on fewer subjects Small N designs

23 Small N designs Disadvantages
Difficult to determine how generalizable the effects are Effects may be small relative to variability of situation so NEED more observation Some effects are by definition between subjects Treatment leads to a lasting change, so you don’t get reversals Small N designs

24 Statistics Mistrust of statistics? It is all in how you use them
They are a critical tool in research Statistics

25 Samples and Populations
Sampling methods Sample Samples and Populations

26 Samples and Populations
2 General kinds of Statistics Descriptive statistics Used to describe, simplify, & organize data sets Describing distributions of scores Inferential statistics Used to test claims about the population, based on data gathered from samples Takes sampling error into account. Are the results above and beyond what you’d expect by random chance? Population Inferential statistics used to generalize back Sample Samples and Populations

27 Samples and Populations
2 General kinds of Statistics Descriptive statistics Used to describe, simplify, & organize data sets Describing distributions of scores Inferential statistics Used to test claims about the population, based on data gathered from samples Takes sampling error into account. Are the results above and beyond what you’d expect by random chance? Population Inferential statistics used to generalize back Sample Samples and Populations

28 Recall that a variable is a characteristic that can take different values.
The distribution of a variable is a summary of all the different values of a variable Both type (each value) and token (each instance) How much do you like statistics? Hate it Love it 5 values (1, 2, 3, 4, 5) 7 tokens (1,1,2,3,4,5,5) 1 5 5 4 1 3 2 Distribution

29 Distribution Many important distributions 5 1 2 5 3 Population Sample
All the scores of interest Sample All of the scores observed (your data) Used to estimate population characteristics Distribution of sample distributions Used to estimate sampling error Population 1 2 3 3 1 5 1 2 3 1 Sample How do we describe these distributions? Use descriptive statistics, focus on 3 properties Distribution

30 Describing Distributions
Properties: Shape, Center, and Spread (variability) Shape Symmetric v. asymmetric (skew) Unimodal v. multimodal Center Where most of the data in the distribution are Mean, Median, Mode Spread (variability) How similar/dissimilar are the scores in the distribution? Standard deviation (variance), Range Describing Distributions

31 Describing Distributions
Properties: Shape, Center, and Spread (variability) Visual descriptions - A picture of the distribution is usually helpful Numerical descriptions of distributions f % 1 (hate) 200 20 2 100 10 3 4 5 (love) 300 30 Describing Distributions

32 Mean & Standard deviation
The mean (mathematical average) is the most popular and most important measure of center. Divide by the total number in the population Add up all of the X’s The formula for the population mean is (a parameter): The formula for the sample mean is (a statistic): Divide by the total number in the sample mean Mean & Standard deviation

33 Mean & Standard deviation
The mean (mathematical average) is the most popular and most important measure of center. The standard deviation is the most popular and important measure of variability. The standard deviation measures how far off all of the individuals in the distribution are from a standard, where that standard is the mean of the distribution. Essentially, the average of the deviations. mean recall Mean & Standard deviation

34 An Example: Computing Standard Deviation (population)
Working your way through the formula: Step 1: Compute deviation scores Step 2: Compute the SS Step 3: Determine the variance Take the average of the squared deviations Divide the SS by the N Step 4: Determine the standard deviation Take the square root of the variance standard deviation = σ = An Example: Computing Standard Deviation (population)

35 An Example: Computing Standard Deviation (sample)
Main difference: Step 1: Compute deviation scores Step 2: Compute the SS Step 3: Determine the variance Take the average of the squared deviations Divide the SS by the n-1 Step 4: Determine the standard deviation Take the square root of the variance This is done because samples are biased to be less variable than the population. This “correction factor” will increase the sample’s SD (making it a better estimate of the population’s SD) An Example: Computing Standard Deviation (sample)

36 Statistics 2 General kinds of Statistics Descriptive statistics
Used to describe, simplify, & organize data sets Describing distributions of scores Inferential statistics Used to test claims about the population, based on data gathered from samples Takes sampling error into account. Are the results above and beyond what you’d expect by random chance? Population Inferential statistics used to generalize back Sample Statistics

37 Inferential Statistics
Purpose: To make claims about populations based on data collected from samples What’s the big deal? Population Sample A Treatment X = 80% Sample B No Treatment X = 76% Example Experiment: Group A - gets treatment to improve memory Group B - gets no treatment (control) After treatment period test both groups for memory Results: Group A’s average memory score is 80% Group B’s is 76% Is the 4% difference a “real” difference (statistically significant) or is it just sampling error? Inferential Statistics

38 Testing Hypotheses Step 1: State your hypotheses
Step 2: Set your decision criteria Step 3: Collect your data from your sample(s) Step 4: Compute your test statistics Step 5: Make a decision about your null hypothesis “Reject H0” “Fail to reject H0” Testing Hypotheses

39 Testing Hypotheses Step 1: State your hypotheses
This is the hypothesis that you are testing Null hypothesis (H0) Alternative hypothesis(ses) “There are no differences (effects)” Generally, “not all groups are equal” You aren’t out to prove the alternative hypothesis (although it feels like this is what you want to do) If you reject the null hypothesis, then you’re left with support for the alternative(s) (NOT proof!) Testing Hypotheses

40 Testing Hypotheses Step 1: State your hypotheses
In our memory example experiment Null H0: mean of Group A = mean of Group B Alternative HA: mean of Group A ≠ mean of Group B (Or more precisely: Group A > Group B) It seems like our theory is that the treatment should improve memory. That’s the alternative hypothesis. That’s NOT the one the we’ll test with inferential statistics. Instead, we test the H0 Testing Hypotheses

41 Testing Hypotheses Step 1: State your hypotheses
Step 2: Set your decision criteria Your alpha level will be your guide for when to: “reject the null hypothesis” “fail to reject the null hypothesis” This could be correct conclusion or the incorrect conclusion Two different ways to go wrong Type I error: saying that there is a difference when there really isn’t one (probability of making this error is “alpha level”) Type II error: saying that there is not a difference when there really is one Testing Hypotheses

42 Error types Real world (‘truth’) H0 is correct H0 is wrong
Type I error Reject H0 Experimenter’s conclusions Fail to Reject H0 Type II error Error types

43 Error types: Courtroom analogy
Real world (‘truth’) Defendant is innocent Defendant is guilty Type I error Find guilty Jury’s decision Type II error Find not guilty Error types: Courtroom analogy

44 Type I error: concluding that there is an effect (a difference between groups) when there really isn’t. Sometimes called “significance level” We try to minimize this (keep it low) Pick a low level of alpha Psychology: 0.05 and 0.01 most common Type II error: concluding that there isn’t an effect, when there really is. Related to the Statistical Power of a test How likely are you able to detect a difference if it is there Error types

45 Testing Hypotheses Step 1: State your hypotheses
Step 2: Set your decision criteria Step 3: Collect your data from your sample(s) Step 4: Compute your test statistics Descriptive statistics (means, standard deviations, etc.) Inferential statistics (t-tests, ANOVAs, etc.) Step 5: Make a decision about your null hypothesis Reject H0 “statistically significant differences” Fail to reject H0 “not statistically significant differences” Testing Hypotheses

46 Statistical significance
“Statistically significant differences” When you “reject your null hypothesis” Essentially this means that the observed difference is above what you’d expect by chance “Chance” is determined by estimating how much sampling error there is Factors affecting “chance” Sample size Population variability Statistical significance

47 (Pop mean - sample mean)
Population mean Population Distribution x Sampling error (Pop mean - sample mean) n = 1 Sampling error

48 (Pop mean - sample mean)
Population mean Population Distribution Sample mean x x Sampling error (Pop mean - sample mean) n = 2 Sampling error

49 (Pop mean - sample mean)
Generally, as the sample size increases, the sampling error decreases Population mean Population Distribution Sample mean x Sampling error (Pop mean - sample mean) n = 10 Sampling error

50 Typically the narrower the population distribution, the narrower the range of possible samples, and the smaller the “chance” Large population variability Small population variability Sampling error

51 Sampling error Population Distribution of sample means
These two factors combine to impact the distribution of sample means. The distribution of sample means is a distribution of all possible sample means of a particular sample size that can be drawn from the population Population Distribution of sample means XC Samples of size = n XA XD Avg. Sampling error XB “chance” Sampling error

52 Significance “A statistically significant difference” means:
the researcher is concluding that there is a difference above and beyond chance with the probability of making a type I error at 5% (assuming an alpha level = 0.05) Note “statistical significance” is not the same thing as theoretical significance. Only means that there is a statistical difference Doesn’t mean that it is an important difference Significance

53 Non-Significance Failing to reject the null hypothesis
Generally, not interested in “accepting the null hypothesis” (remember we can’t prove things only disprove them) Usually check to see if you made a Type II error (failed to detect a difference that is really there) Check the statistical power of your test Sample size is too small Effects that you’re looking for are really small Check your controls, maybe too much variability Non-Significance

54 From last time XA XB About populations Example Experiment:
Group A - gets treatment to improve memory Group B - gets no treatment (control) After treatment period test both groups for memory Results: Group A’s average memory score is 80% Group B’s is 76% H0: μA = μB H0: there is no difference between Grp A and Grp B Real world (‘truth’) H0 is correct H0 is wrong Experimenter’s conclusions Reject H0 Fail to Reject H0 Type I error Type II error Is the 4% difference a “real” difference (statistically significant) or is it just sampling error? Two sample distributions XA XB 76% 80% From last time

55 “Generic” statistical test
Tests the question: Are there differences between groups due to a treatment? H0 is true (no treatment effect) Real world (‘truth’) H0 is correct H0 is wrong Experimenter’s conclusions Reject H0 Fail to Reject H0 Type I error Type II error Two possibilities in the “real world” One population Two sample distributions XA XB 76% 80% “Generic” statistical test

56 “Generic” statistical test
Tests the question: Are there differences between groups due to a treatment? Real world (‘truth’) H0 is correct H0 is wrong Experimenter’s conclusions Reject H0 Fail to Reject H0 Type I error Type II error Two possibilities in the “real world” H0 is true (no treatment effect) H0 is false (is a treatment effect) Two populations XA XB XB XA 76% 80% 76% 80% People who get the treatment change, they form a new population (the “treatment population) “Generic” statistical test

57 “Generic” statistical test
XB XA ER: Random sampling error ID: Individual differences (if between subjects factor) TR: The effect of a treatment Why might the samples be different? (What is the source of the variability between groups)? “Generic” statistical test

58 “Generic” statistical test
XB XA ER: Random sampling error ID: Individual differences (if between subjects factor) TR: The effect of a treatment The generic test statistic - is a ratio of sources of variability Observed difference TR + ID + ER ID + ER Computed test statistic = = Difference from chance “Generic” statistical test

59 Sampling error Population “chance” Distribution of sample means
The distribution of sample means is a distribution of all possible sample means of a particular sample size that can be drawn from the population Population Distribution of sample means XC Samples of size = n XA XD Avg. Sampling error XB “chance” Sampling error

60 “Generic” statistical test
The generic test statistic distribution To reject the H0, you want a computed test statistics that is large reflecting a large Treatment Effect (TR) What’s large enough? The alpha level gives us the decision criterion TR + ID + ER ID + ER Distribution of the test statistic Test statistic Distribution of sample means α-level determines where these boundaries go “Generic” statistical test

61 “Generic” statistical test
The generic test statistic distribution To reject the H0, you want a computed test statistics that is large reflecting a large Treatment Effect (TR) What’s large enough? The alpha level gives us the decision criterion Distribution of the test statistic Reject H0 Fail to reject H0 “Generic” statistical test

62 “Generic” statistical test
The generic test statistic distribution To reject the H0, you want a computed test statistics that is large reflecting a large Treatment Effect (TR) What’s large enough? The alpha level gives us the decision criterion Distribution of the test statistic Reject H0 “One tailed test”: sometimes you know to expect a particular difference (e.g., “improve memory performance”) Fail to reject H0 “Generic” statistical test

63 “Generic” statistical test
Things that affect the computed test statistic Size of the treatment effect The bigger the effect, the bigger the computed test statistic Difference expected by chance (sample error) Sample size Variability in the population “Generic” statistical test

64 Significance “A statistically significant difference” means:
the researcher is concluding that there is a difference above and beyond chance with the probability of making a type I error at 5% (assuming an alpha level = 0.05) Note “statistical significance” is not the same thing as theoretical significance. Only means that there is a statistical difference Doesn’t mean that it is an important difference Significance

65 Non-Significance Failing to reject the null hypothesis
Generally, not interested in “accepting the null hypothesis” (remember we can’t prove things only disprove them) Usually check to see if you made a Type II error (failed to detect a difference that is really there) Check the statistical power of your test Sample size is too small Effects that you’re looking for are really small Check your controls, maybe too much variability Non-Significance

66 Some inferential statistical tests
1 factor with two groups T-tests Between groups: 2-independent samples Within groups: Repeated measures samples (matched, related) 1 factor with more than two groups Analysis of Variance (ANOVA) (either between groups or repeated measures) Multi-factorial Factorial ANOVA Some inferential statistical tests

67 T-test Design Formula: Observed difference X1 - X2 T =
2 separate experimental conditions Degrees of freedom Based on the size of the sample and the kind of t-test Formula: Observed difference T = X X2 Diff by chance Based on sample error Computation differs for between and within t-tests T-test

68 T-test Reporting your results
The observed difference between conditions Kind of t-test Computed T-statistic Degrees of freedom for the test The “p-value” of the test “The mean of the treatment group was 12 points higher than the control group. An independent samples t-test yielded a significant difference, t(24) = 5.67, p < 0.05.” “The mean score of the post-test was 12 points higher than the pre-test. A repeated measures t-test demonstrated that this difference was significant significant, t(12) = 5.67, p < 0.05.” T-test

69 Analysis of Variance XB XA XC Designs Test statistic is an F-ratio
More than two groups 1 Factor ANOVA, Factorial ANOVA Both Within and Between Groups Factors Test statistic is an F-ratio Degrees of freedom Several to keep track of The number of them depends on the design Analysis of Variance

70 Analysis of Variance More than two groups F-ratio = XB XA XC
Now we can’t just compute a simple difference score since there are more than one difference So we use variance instead of simply the difference Variance is essentially an average difference Observed variance Variance from chance F-ratio = Analysis of Variance

71 1 factor ANOVA 1 Factor, with more than two levels XB XA XC
Now we can’t just compute a simple difference score since there are more than one difference A - B, B - C, & A - C 1 factor ANOVA

72 1 factor ANOVA The ANOVA tests this one!! XA = XB = XC XA ≠ XB ≠ XC
Null hypothesis: H0: all the groups are equal The ANOVA tests this one!! XA = XB = XC Do further tests to pick between these Alternative hypotheses HA: not all the groups are equal XA ≠ XB ≠ XC XA ≠ XB = XC XA = XB ≠ XC XA = XC ≠ XB 1 factor ANOVA

73 1 factor ANOVA Planned contrasts and post-hoc tests:
- Further tests used to rule out the different Alternative hypotheses XA ≠ XB ≠ XC Test 1: A ≠ B XA = XB ≠ XC Test 2: A ≠ C XA ≠ XB = XC Test 3: B = C XA = XC ≠ XB 1 factor ANOVA

74 1 factor ANOVA Reporting your results The observed differences
Kind of test Computed F-ratio Degrees of freedom for the test The “p-value” of the test Any post-hoc or planned comparison results “The mean score of Group A was 12, Group B was 25, and Group C was 27. A 1-way ANOVA was conducted and the results yielded a significant difference, F(2,25) = 5.67, p < Post hoc tests revealed that the differences between groups A and B and A and C were statistically reliable (respectively t(1) = 5.67, p < 0.05 & t(1) = 6.02, p <0.05). Groups B and C did not differ significantly from one another” 1 factor ANOVA

75 We covered much of this in our experimental design lecture
More than one factor Factors may be within or between Overall design may be entirely within, entirely between, or mixed Many F-ratios may be computed An F-ratio is computed to test the main effect of each factor An F-ratio is computed to test each of the potential interactions between the factors Factorial ANOVAs

76 Factorial ANOVAs Reporting your results The observed differences
Because there may be a lot of these, may present them in a table instead of directly in the text Kind of design e.g. “2 x 2 completely between factorial design” Computed F-ratios May see separate paragraphs for each factor, and for interactions Degrees of freedom for the test Each F-ratio will have its own set of df’s The “p-value” of the test May want to just say “all tests were tested with an alpha level of 0.05” Any post-hoc or planned comparison results Typically only the theoretically interesting comparisons are presented Factorial ANOVAs

77 Non-Experimental designs
Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each Non-Experimental designs

78 Small N designs What are they?
Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes Even today, in some sub-areas, using small N designs is common place (e.g., psychophysics, clinical settings, animal studies, expertise, etc.) Small N designs

79 In contrast to Large N-designs (comparing aggregated performance of large groups of participants)
One or a few participants Data are typically not analyzed statistically; rather rely on visual interpretation of the data Small N designs

80 = observation Treatment introduced Steady state (baseline) Observations begin in the absence of treatment (BASELINE) Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded Small N designs

81 Small N designs Baseline experiments – the basic idea is to show:
= observation Reversibility Treatment removed Transition steady state Steady state (baseline) Treatment introduced Baseline experiments – the basic idea is to show: when the IV occurs, you get the effect when the IV doesn’t occur, you don’t get the effect (reversibility) Small N designs

82 Unstable Stable Before introducing treatment (IV), baseline needs to be stable Measure level and trend Level – how frequent (how intense) is behavior? Are all the data points high or low? Trend – does behavior seem to increase (or decrease) Are data points “flat” or on a slope? Small N designs

83 ABA design ABA design (baseline, treatment, baseline)
Steady state (baseline) Transition steady state Reversibility ABA design (baseline, treatment, baseline) The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e.g., history, maturation, etc.) There are other designs as well (e.g., ABAB see figure13.6 in your textbook) ABA design

84 Small N designs Advantages
Focus on individual performance, not fooled by group averaging effects Focus is on big effects (small effects typically can’t be seen without using large groups) Avoid some ethical problems – e.g., with non-treatments Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices) Often used to supplement large N studies, with more observations on fewer subjects Small N designs

85 Small N designs Disadvantages
Difficult to determine how generalizable the effects are Effects may be small relative to variability of situation so NEED more observation Some effects are by definition between subjects Treatment leads to a lasting change, so you don’t get reversals Small N designs

86 Some researchers have argued that Small N designs are the best way to go.
The goal of psychology is to describe behavior of an individual Looking at data collapsed over groups “looks” in the wrong place Need to look at the data at the level of the individual Small N designs


Download ppt "Non-Experimental designs: Developmental and Small-N Designs"

Similar presentations


Ads by Google