Samenvatting
Samenvatting Wetenschappelijke Onderzoeksmethoden (INFOWO)
Alle stof voor het eindtentamen van WO
[Meer zien]Voorbeeld 3 van de 19 pagina's
Voorbeeld 3 van de 19 pagina's
In winkelwagengesponsord bericht van onze partner
Voorbeeld van de inhoud
SV WO StatisticsLecture 1- Fundamentals I
Definition & usefulness
➢ Statistics: the study of the collection, organization and interpretation of data.
➢ To contribute to the accuracy and reliability of the evidence we argue for our ideas →
Organize and systematize information data (what happened in a study and communicate it to
others)
➢ To interpret research findings on the basis of numbers: Is there a systematic factor behind
observed differences?
➢ To help bring order out of chaos
What is measured
➢ Objects: things
o Concrete things: people, countries
➢ Properties: Characteristics of objects
➢ Measurements: indicants of properties (of objects)
Useful definitions
➢ Variable: A characteristic or condition that changes or has different
values for different individuals
➢ Data (plural): Measurements or observations. Data set is a collection of
data. Datum (singular) is a single measurement, often referred to as
score or raw score
➢ Descriptive statistics: Statistical procedures to summarize, organize and
simplify data
➢ Inferential statistics: Techniques that study samples and make generalizations about
populations from which the samples were selected
Measurement scales for variables
➢ Nominal
o A set of categories with different names. Comparison operation possible for:
(in)equality: “are two individuals different?
o Values are exhaustive and mutually exclusive
o Bv gender
o But does not specify: “how much different”: no “more than” or “less than”
➢ Ordinal
o A set of categories with different names and organized in ordered sequence (of size,
etc) Comparison operation possible for:
(in)equality: “are two individuals different?
o Order: “more than”, “less than
o Bv highest attained education (primary, high, uni)
o But does not specify: “how much larger” or “how much smaller
➢ Interval
o Ordered categories with in-between intervals of exactly the same size
o Comparison possible for:
▪ (in)equality: “are two individuals different?
▪ Order: “more than”, “less than”
1
, ▪ Distance/difference: “how much more than”/”less than”? (equal differences
between numbers on the scale mean equal differences in magnitude)
o No natural zero value! (= absence of)
o Bv temperatuur, age in categories
o Understand: Zero temperature on this scale does not mean absence of temperature
➢ Ratio
o Interval scale with absolute zero point (can have “zero amount of” type of variable)
allowing to measure ratios
o Comparison possible for:
▪ (in)equality: “are two individuals different?”
▪ Order: “more than”, “less than”
▪ Distance/difference: “how much more than”/”less than”? (equal differences
between numbers on the scale mean equal differences in magnitude)
o Bv gewicht (0 kilos of apples)
o Understand: An individual of 100 kilos (100 from zero) weighs twice as much as
some of 50 kilos → allows measuring ratios
o Has natural zero value, and no negative values!
Summarizing data
Descriptive measures:
➢ Frequency measurements
o Frequency distributions: Help us organize and present way data in a comprehensive
form; An “organized picture of the data”
o Can be presented as: Tables (quickly identify trends),
Pie charts, Graphs
▪ Frequency graph: A picture of the information
available on frequency table
• Absolute frequencies: Firefox: 21 (out
of 500)
• Relative frequencies: 0.042 (also:
proportion) 4.2% (also: percentage)
▪ Graph: Space between adjacent bars
• Visually emphasizing: nominal scale (scale has distinct categories) &
ordinal scale (cannot assume all categories to be of equal size)
▪ Histogram: No space between adjacent bars
• Visually emphasizing: Interval or ratio scale (all categories are of
equal size)
➢ Measure of location/central tendency
o The most common method of summarizing the distribution of some data is a
statistical measure called central tendency
o Purpose:
▪ Identify center of the distribution
▪ Identify best representative score
o You can think of central tendency as the “typical” individual score
o It is an example of “number crunching”:
▪ Take a distribution of many scores
▪ “Crunch” them down to a single value that describes them all
o Mean: Equilibrium or balance point of the distribution (average)
2
, ▪ Thinking of the mean as a balance point helps us visualize
how the distribution is affected when new scores are
added/subtracted
▪ 2 formulas for mean: population and sample
• Population: Set of all the individuals of interest in
a particular study. The size of the population is
usually denoted as: N. The mean µ is a parameter
of the population, and usually unknown.
• Sample: Selection of individuals from a population, usually to
represent the population in a particular study. The size of the sample
is usually denoted as: n. The mean X is a statistic, a value obtained
from the sample, which is used as an estimate for the unknown
population parameter.
o Median: Midpoint of the distribution. Insensitive with respect to ’outliers’ (contrary
to mean)
▪ The Median represents the “midpoint” of the scores in
a distribution when they are listed in order from
smallest to largest. Divides the groups into two groups
of equal size. 50% of scores above, 50% below
median (=50-th percentile P50). No symbol, simply
referred to as median. Same for sample and population
o Mode: Most frequently occuring value
▪ Bimodal/multimodal: more than one value is most
frequent
▪ Most common observation Score with highest
frequency No special notation, referred to as “mode”
Same for population and sample Only central
tendency metric that can describe nominal scale
values A distribution can have multiple modes
(bimodal/multimodal)
➢ Measure of spread/ dispersion / variability
o Variability: “How much” different are the scores of a
distribution and how much they are spread out or clustered
together
▪ Important statistical measure because:
• Describes the distribution
• Tells us how much error to expect when using a sample to represent
the population
o Range: Difference between largest and smallest score of
distribution
▪ Problem using range as measure of variability→
completely determined by the two extreme values and
ignores the other values in the distribution
o Variance: Average of the squared distances (deviations) from
the mean
o Degrees of freedom
▪ Number of scores in sample that are independent
and free to vary
▪ Degrees of freedom df = n − 1
3
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper IsabelleU. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €6,49. Je zit daarna nergens aan vast.
Is Stuvia te vertrouwen?
4,6 sterren op Google & Trustpilot (+1000 reviews)
Afgelopen 30 dagen zijn er 82871 samenvattingen verkocht
Opgericht in 2010, al 14 jaar dé plek om samenvattingen te kopen