Summary
Samenvatting - Mixed Methods Onderzoeksproject (GEO2-7017)
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Samenvatting van Grasple module, Syllabus H4, 5, 6, 7, 8, 10, 11, weblectures en kwalitatieve analyse
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Kwantitatieve Analyse .............................................................................................................................. 1
Grasple ................................................................................................................................................. 1
Samenvatting Syllabus ....................................................................................................................... 13
Weblectures ....................................................................................................................................... 22
SPSS (werkcolleges) ............................................................................................................................ 26
Kwalitatieve Analyse .............................................................................................................................. 27
Werkcolleges ...................................................................................................................................... 37
Multimethod research: de toepassing van meer dan één methode om een onderzoeksvraag te
beantwoorden -> breed scala aan onderzoeksbenaderingen.
Mixed method onderzoek wordt soms specifiek gebruikt om hybride toepassingen te beschrijven
waarbij twee of meer benaderingen tegelijkertijd worden gebruikt. Hybriditeit: één enkel
onderzoeksinstrument dat verschillende soorten gegevens verzamelt. Kan ook verwijzen naar het
gebruik van één enkel onderzoeksinstrument om gegevens te verzamelen voor zowel kwalitatieve als
kwantitatieve gegevensanalyse.
Weblecture: conceptueel model en hypothesen
Conceptueel model: schematische weergave werkelijkheid (blokkenstructuur, met pijlen;
trechtervorm) -> bestaat uit: alle relevante begrippen uit de vraagstelling en alle relevante variabelen
en concepten + de verwachte relaties daartussen (samenhang/causaliteit, directe relaties/indirecte
relaties (mediator) of interacties (moderator), pas op voor schijnrelaties
Kwantitatieve Analyse
Grasple
Beschrijvende Statistiek
- Populatie: gehele groep waarover je uitspraken wilt doen (Griekse symbolen: μ (population
mean) and σ (population standard deviation)
, - Steekproef: specifieke subgroep die je daadwerkelijk onderzoekt/data verzamelt (Roman
symbols like X̄ (sample mean) and s (sample standard deviation)
Beschrijvende statistiek: kenmerken van dataset samenvatten, dus over de gehele populatie
Toetsende statistiek: hypothese toetsen/bepalen of je data generaliseerbaar zijn naar een bredere
populatie
Measurement Levels
- The measurable outcomes of a study are called variables
- Variables have four possible measurement levels:
- Categorical: Nominal, Ordinal
- Continuous: Interval, Ratio
Frequency tables
- Absolute frequency: the number of times you have observed that value (count)
- Relative frequency: the number of times you have observed a value, expressed as a
percentage (relative to the total number of observations)
Mode
- Count how many times each value comes up in your dataset
- The value that occurs most is called Mode
- Are there two values with the highest frequency? → two modes
Symbols
- X: values/observation
- Σ (sigma): adding values (the sum)
- N: number of values
- M: average (mean)
- i: index, indicates which particular value you need
Mean
- Mean = add (all values) / number of values
,Quartiles
- Quartiles are the number that split your data into four equal parts. Every part contains 25%
of the data
- You can find the quartiles by splitting your data into two using the median. Then you can
take the median of those parts as well and you get all the quartiles.
Standard Deviation
- Calculate the Sum of Squares: SS (using the table: Mean, differences, square those, sum the
squares)
- Calculate the Variance: σ² = SS / n
- Calculate the Standard Deviation: σ = √ σ²
Graphs
Box Plot
- Variation: how much the data differs from the measure of centre
- A good way to visualise both the measure of centre and the variation is the box plot
- In box plot:
- The middle bar is the median
- The box extends from the median of the left half to the median of the right half of the data
- The horizontal lines indicate where the rest of the data is
Coefficient of Variation
- The coefficient of variation is the standard deviation divided by the mean
Inferential Statistics
Sampling Distribution
Sampling Distribution: a distribution of sample means
Population, Sample and Sampling Distribution
Three different type of distributions:
- Population distribution: how the population is distributed, this is what we don’t always know
is inferential statistics, so instead we take a sample
- Sample distribution: a graph that displays the distribution of a single sample, this distribution
is knows
- Sampling distribution: the distribution of a lot of sample means, this can be constructed by
doing a lot of experiments. Usually you estimate it based on your 1 sample
, Standard Error
A sample-mean will generally differ slightly from the real population-value. Because we're taking a
sample, a slight difference will arise, an error, that we cannot prevent.
The mean of all those errors we call the standard error.
Standard error: measures the average variation between the sample means compared to the
population mean. The standard error is therefore very similar to the standard deviation, that
measures the average distance of a specific value to the sample mean.
A very important difference is that the standard error measures the average difference between the
sample-means ‾X and the population-mean. The standard deviation measures the average difference
between individual values X and the sample mean.
However, often the standard deviation of the population σ is unknown. Then you replace the
standard deviation of the population with the standard deviation of the sample. → estimated
standard error.
Point Versus Interval Estimates
- You have point- and interval estimates.
- A point-estimate is one value.
- An interval-estimate is where you state: the value will be somewhere between one value and
another value.
- It’s best practice to always use interval-estimates.
The interval estimate covers a range of values, it is more likely that the value you are trying to predict
will be found in this range than at a single point. → it shows how certain you are.
Interval-estimate is preferred.
- Visually
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