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Quantitative MethodsFor Social Sciences
Lionel Nesta
Observatoire Français des Conjonctures Economiques
CERAM February-March-April 2008
Objective of The Course The objective of the class is to provide students with a set of techniques to analyze
quantitative data. It concerns the application of quantitative and statistical approaches as
developed in the social sciences, for future decision makers, policy markers, stake
holders, managers, etc.
All courses are computer-based classes using the SPSS statistical package. The objective
is to reach levels of competence which provide the student with skills to both read and
understand the work of others and to carry out one's own research.
Class Password: stmarec123
Examples Rise in biotechnology
Should the EU fund fundamental research in biotechnology?
Has biotechnology increased the productivity of firm-level R&D?
Did it increase the speed of discovery in pharmaceutical R&D?
Increasing university-industry collaborations
Does it facilitate innovation by firms?
Does it increase the production of new knowledge by academics?
Does it modify the fundamental/applied nature of research?
Examples Economic (productivity) Growth
Does it come mainly from new firms or improving existing firms?
Is market selection operating correctly?
Why do good firms exit the market?
How does the organisation of knowledge impact on performance?
How do knowledge stock and specialisation impact on productivity?
How do firms enter into new technological fields?
Do firms diversify in new technologies/businesses purposively?
Structure of the Class
Class 1 : Descriptive Statistics
Class 2 : Statistical Inference
Class 3 : Relationship Between Variables
Class 4 : Ordinary Least Squares (OLS)
Class 5 : Extension to OLS
Class 6 : Qualitative Dependent variables
Structure of the Class
Class 1 : Descriptive Statistics
Mean, variance, standard deviation
Data management
Class 2 : Statistical Inference
Class 3 : Relationship Between Variables
Class 4 : Ordinary Least Squares (OLS)
Class 5 : Extension to OLS
Class 6 : Qualitative Dependent variables
Structure of the Class
Class 1 : Descriptive Statistics
Class 2 : Statistical Inference
Distributions
Comparison of means
Class 3 : Relationship Between Variables
Class 4 : Ordinary Least Squares (OLS)
Class 5 : Extension to OLS
Class 6 : Qualitative Dependent variables
Structure of the Class
Class 1 : Descriptive Statistics
Class 2 : Statistical Inference
Class 3 : Relationship Between Variables
ANOVA, Chi-Square
Correlation
Class 4 : Ordinary Least Squares (OLS)
Class 5 : Extension to OLS
Class 6 : Qualitative Dependent variables
Structure of the Class
Class 1 : Descriptive Statistics
Class 2 : Statistical Inference
Class 3 : Relationship Between Variables
Class 4 : Ordinary Least Squares (OLS)
Correlation coefficient, simple regression
Multiple regression
Class 5 : Extension to OLS
Class 6 : Qualitative Dependent variables
Structure of the Class
Class 1 : Descriptive Statistics
Class 2 : Statistical Inference
Class 3 : Relationship Between Variables
Class 4 : Ordinary Least Squares (OLS)
Class 5 : Extension to OLS
Regressions diagnostics
Qualitative explanatory variables
Class 6 : Qualitative Dependent variables
Structure of the Class
Class 1 : Descriptive Statistics
Class 2 : Statistical Inference
Class 3 : Relationship Between Variables
Class 4 : Ordinary Least Squares (OLS)
Class 5 : Extension to OLS
Class 6 : Qualitative Dependent variables
Linear probability model
Maximum likelihood (logit, probit)
Class 1Descriptive Statistics
Types of DataDescriptive statistics is the branch of statistics which gathers all techniques used to describe and summarize quantitative and qualitative data.
Quantitative data Continuous Measured on a scale (value its the range) The size of the number reflect the amount of the variable Age; wage, sales; height, weight; GDP
Qualitative data Discrete, categorical The number reflect the category of the variable Type of work; gender; nationality
Descriptive Statistics
All means are good to summarize data in a synthetic way: graphs; charts; tables.
Quantitative data Graphs: scatter plots; line plots; histograms Central tendency Dispersion
Qualitative data Graphs: pie graphs; histograms Tables, frequency, percentage, cumulative percentage Cross tables
Central Tendency and Dispersion A distribution is an ordered set of numbers showing how many
times each occurred, from the lowest to the highest number or the
reverse
Central tendency: measures of the degree to which scores are
clustered around the mean of a distribution
Dispersion: measures the fluctuations around the characteristics of
central tendency
In other words, the characteristics of central tendency produce
stylized facts, when the characteristics of dispersion look at the
representativeness of a given stylized fact.
Central Tendency The mode
The most frequent score in distribution is
called the mode.
The median The middle value of all observed values, when
50% of observed value are higher and 50% of
observed value are lower than the median
The mean The sum of all of the values divided by the
number of value 1
1
i n
ii
X xN
The mode, the mean and the median ore equal if and only of the distribution is symmetrical and unimodal.
Dispersion
22 1
i n
ii
x X
N
The range
Difference between the maximum and
minimum values
The variance Average of the squared differences between
data points and the mean (average)
quadratic deviation
The standard deviation Square root of variance, therefore measures
the spread of data about the mean,
measured in the same units as the data
22 1
i n
ii
x X
N
max min R x x
Dispersion
22 1
i n
ii
x X
N
The range
Difference between the maximum and
minimum values
The variance Average of the squared differences between
data points and the mean (average)
quadratic deviation
The standard deviation Square root of variance, therefore measures
the spread of data about the mean,
measured in the same units as the data
22 1
i n
ii
x X
N
max min R x x
Research Productivity in the Bio-pharmaceutical Industry EU Framework Programme 7
Stylised Facts about Modern Biotech1. Innovations emerge from uncertain, complex processes
involving knowledge and markets: Roles of networks.
2. Economic value is created in many ways – globally and in geographical agglomerations
3. Various linkages exist among diverse actors (LDFs, DBFs, Univ, Venture Capital) in innovation processes, but the firm plays a particularly important role.
4. Regulations, social structures and institutions affect on-going innovation processes as well as their impacts on society: Importance of IPR.
SPSSStatistical Package for the Social Sciences
The SPSS software Statistical Package for the Social Sciences (1968)
Among the most widely used programs for statistical analysis
in social sciences.
Market researchers, health researchers, survey companies,
government, education researchers, and others.
Data management (case selection, file reshaping, creating
derived data)
Features of SPSS are accessible via pull-down menus
The pull-down menu interface generates command syntax.
SPSS : Opening SPSS
SPSS : Importing data
SPSS : Importing data
SPSS : Importing data Settings in the “import text” dialogue box
No predefine format (1)
Delimited (2)
First lines contains the variable names (2)
One observation per line // all observations (3)
Tab delimited only (4)
Finish (6)
SPSS windows SPSS has opens automatically windows
The datasheet window
Observe, manage, modify, create, data
The results window
Everything you do will be stored there
The syntax window can be opened
SPSS : Data sheet (1)
SPSS : Data sheet (2)
SPSS : Result / Journal
SPSS : Saving data
SPSS : working, at last!
Recoding Variables Changing existing values to new values (biotechnologie → DBF,
pharmaceutique → LDF)
1
2
3
Computing New Variables Taking logarithm (normalization of continuous variables)
1 2
Creating Dummy Variables Taking logarithm (normalization of continuous variables)
1
2
3
Computation of Descriptive Statistics
1
2
3
Descriptive Statistics
Statistiques descriptives
457 286 0 286 11.92 22.901 524.470
457 35788473.97 4422.18 35792896.15 4358371.54 6086530.85 3.705E+013
457 1917997.980 858.53204 1918856.512 330236.630 405160.516 164155043889
457 2.0235309 -1.1298400 .8936909 -.056808610 .3374751802 .114
457 1 0 1 .63 .482 .232
457 1 0 1 .37 .482 .232
457
patent
assets
rd
spe
pharma
biotech
N valide (listwise)
N Intervalle Minimum Maximum Moyenne Ecart type Variance
Splitting Database
1 2
Descriptive Statistics (by type)
Statistiques descriptives
167 202 0 202 12.11 21.066 443.764
167 2442619 4422.18 2447041 342934.49 478511.938 2E+011
167 495443.5 858.53204 496302.1 58116.590 88638.5347 8E+009
167 1.7544527 -1.12984 .6246127 -.10630582 .343286812 .118
167 0 0 0 .00 .000 .000
167 0 1 1 1.00 .000 .000
167
290 286 0 286 11.81 23.929 572.609
290 4E+007 218006.47 4E+007 6670709.4 6605972.68 4E+013
290 1912600 6256.248 1918857 486940.24 432514.940 2E+011
290 1.6904465 -.7967556 .8936909 -.02830504 .331330781 .110
290 0 1 1 1.00 .000 .000
290 0 0 0 .00 .000 .000
290
patent
assets
rd
spe
pharma
biotech
N valide (listwise)
patent
assets
rd
spe
pharma
biotech
N valide (listwise)
typeDBF
LDF
N Intervalle Minimum Maximum Moyenne Ecart type Variance
Assignments Compute logarithm for all quantitative variables patent, assets,
rd, and name them lnpatent, lnassets and lnrd, respectively.
Compute descriptive statistics for both LDFs and DBFs.
Draw conclusion by comparing means.
Logarithm
log 1log
x xx
x x x
Normalization
Taking the logarithm is a transformation which usually normalize distribution.
Elasticities http://en.wikipedia.org/wiki/Elasticity_(economics)
A change in log of x is a relative change of x itself.
Cobb-Douglas production function
