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Summary - Quantitative methods (MAN-BCU2030EN)
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my summary from the (lecture) lectures of Quantitative Methods given in the pre-master of Spatial Planning at Radboud University.
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Quantitative MethodsResume
Exam
On the exam:
The (basic) principles of the different (type of) techniques (of all theme blocks);
Interpretation of outcomes/ results;
No (complex) computations (no calculator allowed).
Don’t forget these articles:
Knegt, de et al. (2010) spatial autocorrelation and the scalling for species-environment
relationships;
Kumar (2007) spatial sampling design for a demographic and health survey.
Known for the exam:
1. What are the differences between the classic regression analysis and structural equation
modelling?
2. What are the advantages of structural equation modelling with respect to classic
regression analysis?
3. Concepts:
o Latent variables, manifest variables (linked to empirical observations)
o Endogenous variables, exogenous variables (linked to the conceived model)
o Recursive and non-recursive models
o Unstandardized, standardized (regression) coefficients
o Direct, indirect and total effects, suppression
o Identification, identification rules
o Modification index
o Critical ratio
4. Model evaluation:
o Chi-squared, df, p, o-close, (A)GFI
o T-test, p
5. Model improvement
o Reduction (CR)
o Extending (MI)
6. Steps
o Analyses of the measurement models;
o Analyses of the structural models.
7. Applying the model a specific process (e.g.) other decision-making processes).
,Theme 1: Intro, Variables and Techniques, OLS
General considerations
Deductive and inductive logic can be envisioned integral parts of an overall cycle of research as
both connect theory with empirical observations
When researchers use deductive logic they first start with a broad theory and then
articulate specific hypotheses to test that theory and then systematically collect data by
which they can test their hypothesis > qualitative research;
When researchers use inductive logic they start with observations and from there start
to see patterns and form there start to develop theory > quantitative research.
The unit of analysis is the level from which the data will be gathered e.g. individual, family,
school, school district. There may be different units of analysis:
One for the dependent variable;
One for the independent variable.
Probability sampling:
Simple Random Sampling
o Selecting a sample from the population so all in the population have an equl
chance of being selected.
o Each *name* has an equal chance of being drawn.
Systematic sampling
o Choosing every ‘’nth’’ individual or site in the population until the desired
sample size is achieved;
o An alphabetic listing, choosing every 10th.
Stratified Sampling
o Stratifying the population in a characteristic (e.g. gender) then sampling from
each stratum;
o Divided the sample group, e.g. in man and woman.
Multistage Cluster Sampling
o A sample chosen in one or two stages because the population is not easily
identified or is large;
o First your cluster the samples, and that you set different stages.
Nonprobability sampling
Convenience sampling
o Participants are selected because they are willing and available to be studied;
o You choose a sample that is convenience;
Snowball sampling
o The researcher asks participants to identify other participants to become
member of the sample;
o You use one student, and ask them to refer to another person.
The population is 9000 students in a particular school. And there are 6000 boys and 3000
girls. You want to get the proportional stratification sample from all the boys and girls.
Boys 200
N = 6000 0.66 of pop.
Girls 100
N = 3000 0.33 of pop.
N = 9000 Sample = 300
,Linking data collection to variables and questions.
1. Identify the variable;
2. Operationally define the variable;
3. Locate data (measures, observations, documents with questions and scales);
4. Collect data on instruments yielding numeric scores.
An instrument is a tool for measuring, observing, or documenting quantitative data. Types of
instruments:
Performance measures (e.g. test performance);
Attitudinal measures (measures feelings toward educational topics);
Behavioral measures (observations of behavior);
Factual measures (documents, records).
Probability sampling is the selection of individuals from the population so that they are
representative of the population.
Nonprobability sampling is the selection of participants because they are available, convenient
or represent some characteristics the investigator wants to study’s.
Validity is whether an instrument measures what it sets out to measure. Validity is truth/
conclusions are accurate. Types of validity:
Content (representative of all possible questions that could be asked);
Criterion-referenced (scores are a predictor of an outcome or criterion they are expected
to predict);
Construct (determination of the significance, meaning, purpose, and use the scores).
Reliability is whether an instrument can be interpreted consistently across different situations.
Reliability is the scores from measuring variables that are stable and consistent. Types of
reliability:
Test-retest (scores are stable);
Alternate forms (equivalence of two instruments);
Alternate forms and test-retest;
Inter-rater reliability (similarity in observation of a behavior by two or more
individuals);
Internal consistency (consistent scores across the instrument).
, Variables
There are different variables types and methods of analysis:
1) Response variable (dependent variable) VS explanatory variable (independent variable).
Dependent variable: a variable through to be affected by changes in an independent variables.
You can think of this variable as an outcome. The dependent variable is the effect. Its value
depends on changes in the independent variable.
Independent variable: is a concept that you are using to explain the concept. The independent
variable is the cause. Its value is independent of the other variables. e.g.: size, age.
2) Manifest VS latent variable
Manifest variable: is a concept that is measurable which is directly used in the analysis, think of
age or gender.
This is about statistics. So we can only do modelling if we have numbers. So the variables have
to be manifest. That means that these research phenomenon needs to be directly observable.
Latent variable: is a concept that is not directly observable like globalizing – international
migration.
If we want to study globalizing, we need characteristics of globalizing that we can observe. e.g.
tourism, international trade, etc.
It is import to know which variables you need to take and combine a level of measurement.
There are three different levels of measurement:
Nominal (categorical); categories that describe traits or characteristics participants can
check. The values are equal like the term ‘color’. We use a frequency and there is no
order or mean. E.g. gender, age, color, etc.
Ordinal (categorical); participants rank order a characteristic, trait, or attribute. The
values are not equal like the term ‘satisfaction or rank’. Ordinal values have an order,
but this order may not be equal. E.g. happy, happier, happiest.
Interval (continuous); provides ‘’continuous’’ response possibilities to questions with
assumed equal distance. The interval scale has no zero point and can be measured. e.g.
temperature in Celsius or Fahrenheit is an interval scale because zero is not the lowest
possible temperature.
Ratio (continuous): A scale with a true zero and equal distance among units. The values
can be measured. e.g.: distance, height.
Dependent variable Method
Metric (interval + ratio) Linear regression (OLS)
Ordinal Ordered logit regression
Nominal Multinomial logistic regression
- Binary/ binomial Logistic regression
Changes in time (metric) Time series analysis
Spatial dimension Spatial analyses
The strength is the extent to which a model (e.g. a regression-equation) explains the
observations (e.g. expressed by R2). The nature expresses what the effect is of an independent
variable on a dependent one (e.g. through a regression coefficient). (Correct as well, strength
(plus direction) can be tested through correlation, the nature by regression analysis).
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