Download 2 - Innoevalua

School of Psychology
Dpt. Experimental Psychology
Design and Data Analysis
in Psychology I
2010-11
Mila Sánchez Martín
Course presentation
 Course
teachers:
Vicente Manzano Arrondo
 Hassan Fazeli Khalili
 Mª Angeles Arias Velarde
 Milagrosa Sánchez Martín

The idea is that
we can
understand us!!
2
Books
3
Diseño y Análisis de Datos en Psicología I
SUMATORIOS
DOCUMENTO MONOGRAFICO I
4
Diseño y Análisis de Datos en Psicología I
CÓMO DESPEJAR
UNA
VARIABLE
DOCUMENTO MONOGRAFICO II
5
Diseño y Análisis de Datos en Psicología I
FORMULARIO
DOCUMENTO MONOGRAFICO III
6
Class schedule
Monday
8.30 – 9.30
Tuesday
Wednesday
Thursday
BG (6)
BG (6)
9.30 – 10.30
10.30 –
11.30
MG A1 (a)
11.30 –
12.30
MG A2 (a)
12.30 –
13.30
13.30 –
14.30
Friday
Office Hours
Office Hours
SGA2b
SGA1b
(10)
SGA2a
SGA1a
(10) 7
Course objectives

Establish the essential foundations to enable the student of
Psychology to conduct the initial data analysis coming from
psychological studies.

In the early chapters we develop the concepts of data description,
initial steps of any statistical study: the collection, tabulations and
graphical presentations of data, and the achievement of its
fundamental characteristics. It's known as descriptive analysis.

In the second set of chapters we propose a group of concepts and
procedures with the objective that the student understands the
logical development of what is known as inferential statistics
(estimation theory and statistical decision). These contents are basic
for the resolution of any research problem by sampling.
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Chapter 1: fundamental concepts
Role of data analysis in Psychology as a science and
professional practice.


General concepts:
- Statistical population.
- Sample.
- Statistical.
- Parameter.
Variables and their clasification:
- Nominal or qualitative variable.
- Ordinal variable.
- Discrete and continuous quantitative variable.
9
Chapter 2: Frequency distribution and
graphic representation

Introduction.

Frequency distributions.

Graphic representations:



Qualitative variables
Quantitative variables.
Properties of frequency distributions.
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Chapter 3: Basic statistical
characteristics I

Central tendency measures:
- Arithmetic mean.
- Median.
- Mode.

Comparison between central tendency
measures.

Position measures based on quantiles.
11
Chapter 4: Basic statistical
characteristics II



Measures of variation or dispersion (justification
and adequacy):
- [total] range.
- Semi-interquartile range.
- Variance and standard deviation.
Other measures of variation :
- Pearson’s coefficient.
- Quasi-variance.
Measures of Skewness and Kurtosis (justification):
- Skewness.
- Kurtosis or "peakedness" .
12
Chapter 5: Z scores and the
normal curve
Concept, implications and justification.
 Raw scores, differentials and standard
scores (or z scores).
 Properties of these scores.
 Normal curve.
 Standard normal curve.
 Some applications.

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Chapter 6: Inferential statistics I





Basic diagram of the dynamic relationship
between sample and population.
Sampling error and statistical reliability.
Sampling distribution: concept and empirical
construction.
Concept,
uses
and
implications
of
the
mathematical expectation, bias and standard
error.
Some sampling distributions:
- Sampling distribution of the mean.
- Sampling distribution of the proportions.
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Chapter 7: Inferential statistics II







Introduction.
Punctual estimation: concept and consequences.
Interval estimation: accuracy, accuracy error and
risk.
Probability interval.
Confidence interval.
Estimation of means and proportions.
Calculate the sample size for estimating means
and proportions.
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Chapter 8: Inferential statistics III





Introduction.
State and significance of the null hypothesis.
Alpha risk: concept, associated concepts, decision
about their amount and timing for the decision of
alpha.
Statistical decision based on standardized
distances.
Some cases based on normality assumption:
comparison of means and proportions.
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Chapter 9: Covariation for
nominal variables




Comparison of two observed proportions in
independent and dependent groups.
Contingency table as a bivariable frequency
distribution: expected frequencies, residuals and
standardized residuals.
Chi-square as a measure of independence:
Pearson test and distribution of associated
probability.
Calculation, interpretation and limitations.
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3 blocks of content:

Block 1: capters 2, 3 and 4

Block 2: chapters 5 and 6

Block 3: chapters 7, 8 and 9 (if it’s possible)
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Evaluation system


Written final exam : 0-8 pts.
Paper in small group (optional): 0-2 pts.
I will commet the details in practice hour
Voluntary testing (mid semester): 1 pt. –is added to the
final mark once required tests are passed-.


Only includes Block 1 (chapters 2, 3 and 4)
To pass the course: minimum of 5 pts. in the final mark
(sum of final exam and paper in small group); it’s essential to
obtain at least 4 pts. in the written final exam.

On the day of the exam the student will have a standard
form, which will include the necessary statistical tables, and a
calculator.
19

Classroom methodology (I)

Big group (BG):
 Master classes
 Approach problems and issues
 It includes all registered

Medium group (MG):
 Solve case studies
 Practice with computer
 It includes half of the class



A1: since Armesto Luque to Lopez Cabrera (inc)
A2: since Lopez Navarro to Vazquez Naharro (inc)
Small group (SG): Details in practice hour
 Paper in small group (10 persons aprox.)
 Presentation of papers for each chapter in writing and in person (every 2
weeks aprox.)
 Presentation of assignments in class for each block (3 sessions)
 In theory it includes quarter of the class every two weeks
 Proposition: half of the class (A1 and A2 complete) every weeks. Last hour
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reserved for exposure hours and doubts.
Classroom methodology (II)

Theory and practice at the same time (more or less)

When we finish chapter’s practice: next week,
exercise delivery (SG)

When we finish one block of contents: exposure
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Schedule (approx.)

Presentation of Block I SG (2h): 1st week
April

Presentation of Block II SG (2h): 3rd week
May

Presentation of Block III SG (2h): last
week

Voluntary testing: last week April
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Guidelines to study the subject

Attending class

Active approach: study from the first class
To do the exercises
by comprehensive
learning

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Chapter 1
General concepts
Introduction

What does a subject course like this represent in a degree
course like this?

I'll be a psychologist :


Generator of knowledge (researcher)
Consumer of knowledge generated by others (clinical psychologist, human
resources, school, ...)

A good psychologist can’t perform their profession properly
without the necessary knowledge to make investigations or to
consume comprehensively those made by other researchers.

No matter what we do as a psychologist in the future. If we
take our work with professionalism, we will be almost
continuously collecting data, analyzing and drawing
conclusions.
25
Data analysis
Univariate
• Data analysis of only one
variable
• Data analysis of two variables
Bivariate
• Data analysis of more than one
variable
Multivariate
26
General concepts
1. Population
2. Sample
3. Parameter
4. Statistics
5. Subjects
6. Variables
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1. Statistical population (N)

Set of all elements (people, animals, things ...) that have one
or more common characteristic or property:

Freshman Psychology degree during 2010/11




Each student would be an element of the population
You could consider: size, age, IQ, etc.
Dogs in foster in Seville
Apartments for sale in Murcia

Each element of the population: individual, subject or case

Finite or infinite population

The usual practice is to work with finite populations, but if the
number of elements is large it is considered as infinite.
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2. Statistical sample (n)

Subset of a population

Requirements of a sample to draw conclusions
(inferences) in the population:

Representative of the population (true reflection)
Appropriate method of elements selection (that all
elements could have been chosen as sample items)

Number of elements big enough


Big sample (n≥30) and small (n<30) –only with
educational purposes29
3. Statistical population


Values that determine the descriptive properties of a population
Not usually known:


N usually numerous: not profitable to work with them (excepc. CI)
Are changing (average weight of Spanish)
Using samples, through their properties, estimate the population
 Proportion (
) -pi-, standard deviation ( σ ) -sigma-, mean ( μ )
-mu-, etc.




2






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4. Statistics
 Descriptive properties of a sample
Although influenced by errors of different types, are used to
determine the approximate value of the parameters

MEDIAN
Mdn
MODE
Mo
MEAN
STANDARD
DEVIATION
X
S
VARIANCE
S2
PROPORTION
p
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5. Subjects
Don’t have the same features in the same
way nor in the same amount
 Height: 1.50, 1.85, 1.63, etc.

-
Individuals
Subjects
Cases
Participants
-
People
Animals
Things
Numbers
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6. Variables

A set of different values

A constant has only one value

Height, marital status, size, etc.

Can be measured with statistical techniques

There is a classification of variables according to
the type of mathematical operations that we are
allowed to do with the assigned numbers
(Stevens’ classification)
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Stevens’ classification

Nominal scale: nominal variable

Ordinal scale: ordinal variable

Interval scale: quantitative variable

Ratio measurement: quantitative variable
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Nominal variable




Do not take numerical values, as they describe qualities
We can assign numbers
Measurement level allows us to identify or distinguish between
elements
Dichotomous nominal variable :





Habitat: rural - urban.
Answer to an item: True – False.
Contraceptives: Yes – No.
Sex: Man - Woman.
Polytomous nominal variable :




Political group : PA – PP – PSOE – IU – CIU ...
Marital status: M – D – W.
Type of neurosis: hysterically Obsessive-phobic-Depressive
Smoking: Smoking - No Smoking – former smokers
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Ordinal variable (quasiquantitative)

One that can sort its elements, not only to distinguish them.

Used as an ordinal measurement scale.

When in an ordinal variable the differences between immediate
values in order remain fairly constant, these can be treated as
an interval variable.

Measurement level allows us to: differentiate between values –
nominal scale- and order (higher, lower or equal)
Examples:





Social Class: Low - Medium - High.
Satisfaction: High - Medium - Low.
Level of agreement: Do not agree ... Agree
Opinion: Disagree Total (1) ... Total agreement (5)
36
Quantitative variable

Can be measured by two types of scale:

Interval scales: for quantitative variables in that the distance
between any two consecutive values is constant respect to a
particular property. (5-4)=(28-27)
Ratio scale: for quantitative variables in that, in addition to the
above, admit the existence of absolute 0, thus establishing ratios
between different values. 15/3= 5



Distinction between scales: absolute zero (absence of the feature
that measures the variable) or relative.
Discrete quantitative variable (you can only take integer
values) or continuous (may be infinite number of values
between two consecutive numbers).
Examples:

Income (in thousands of euros), Temperature (in degrees), Weight (in Kilos), length
(in meters, cnt., mm.) Height (in meters, cnt., mm), No. of correct answers Reaction
times (in milliseconds ...), Age (years, months, days), work experience (years, 37
months ...), CI.
Data analysis
Univariate
Bivariate
Multivariate
38
Variables presentation
Identify the types of variables
Case
Sex
Treatment
(Drug)
Patient check
Number of
attacks
Heart rate
Walk
away
1
1
3
1
1
72
185
2
2
1
2
2
75
174
3
2
2
3
4
86
120
4
1
1
4
5
92
112
5
1
2
3
3
84
134
6
1
3
2
3
81
123
7
2
3
1
2
74
189
8
1
2
4
5
71
140
9
1
1
3
3
80
166
10
1
1
2
2
77
158
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Case
Sex
Treatment
(Drug)
Patient check
Number of
attacks
Heart rate
Walk
away
1
Men
C
Much better
1
72
185
2
Women
A
Better
2
75
174
3
Women
B
Equal
4
86
120
4
Men
A
Worse
5
92
112
5
Men
B
Equal
3
84
134
6
Men
C
Better
3
81
123
7
Women
C
Much better
2
74
189
8
Men
B
Worse
5
71
140
9
Men
A
Equal
3
80
166
10
Women
A
Better
2
77
158
40
Issues
41
1st issue

Usually in a graphical representation of bar chart for a
frequency distribution of a variable shows the following
disposition:
1) Values on the vertical axis and frequency on the
horizontal axis.
2) Values on the vertical axis and cumulative frequency on
the horizontal axis.
3) Values on the horizontal axis and cumulative frequency
on the vertical axis.
4) Values on the horizontal axis and absolute frequency on
the vertical axis.
42
2nd issue

The mean for these data "2, 4, 6 and 8"
is:

1) 3.

2) 4.

3) 5.

4) 8.

5) Depends on the value of the variance (not given).
43
3rd issue

What is the mode in the next set of
data: 2, 4, 4, 4, 6, 8?
1) 2, because it is the lowest value.
2) 8, because it is the highest value.
3) 4, because it is the most frequent value.
4) 5, because the value is more focused.
5) This data set is not a mode.
44
4rd issue
 What
rates you can consider like a
dispersion measure
1) The median.
2) The variance.
3) The mean.
4) Any of the above.
45
5thd issue

If a distribution is very homogeneous,
what effect can be expected in their
index?
1) Large standard deviation.
2) Small standard deviation.
3) Large median.
4) Small median.
46