begriffe

Question 1

validity

Answer 1

extent to which a concept/measurement is well-foundend and likely corresponds accurately to the real world

internal vs external validity

Question 2

internal validity

Answer 2

the obtained effect of x on y for your sample is the correct effect for the sample

-> generalization of causal findings to all cases WITHIN the sample

how to obtain:
-empirical model is correctly specified, estimators are unbiased
-> changes in the dependent variable are attributed to the independent variable (and no other factors ->challenge to eliminate that chance)

Question 3

external validity

Answer 3

obtained effect of x on y in the sample is the correct effeft of x on y in the population P

-> generalization of causal findings to other cases not included in the sample -> the overall population

how to obtain:
-enough cases
-sample represents the population in all relevant characteristics

Question 4

why is validity important

Answer 4

-theory and findings need to show a causal effect for the research to be relevant
-stakeholders need to know whether it also holds for other cases
-in practice: experiments usually of low external but high internal validity or neither perfect internal nor external validity

Question 5

validity vs reliablility

Answer 5

reliability is the degree of precision with wich a specific aspect is measured

Question 6

advantages of scientific observation

Answer 6

systematic approach of observing and generating information
-objectivity as oppose to selective set ob observations
-avoidance of “filling in” information
-verifiability

Question 7

population

Answer 7

all observational unit to whom the theory is assumed to apply

Question 8

sample

Answer 8

a subset of the theoretically-defined population for which data is assessed
for reasons of validity, we want this subset to be representative of the population

Question 9

descriptive statistics

Question 10

inferential statistics

Question 11

what is data

Answer 11

quantified information
information for one single case: date point

manifest variables: directly observable variables (zb body height)
latent variables: abstract concepts only observable through manifest indicators (zb democracy etc)

Question 12

data types by source

Answer 12

source:
observable world -> observational data
field or lab experiment -> experimental data
an algorithm -> simulated data

Question 13

data processing

Answer 13

-> to eliminate sources of error

processing includes:
-reduction of measurement error
-addressing of inter-coder reliablitity
-elimination of missing data points
-identification of outliers

Question 14

how to measure data

Answer 14

measurements require
-measurement scale
-measurement unit
-measurement instrument

also includes
-counts
-quantifiactions

Question 15

types of variables

Answer 15

can be descriped by three elements: instrument, measurement unit, scale

Question 16

variables by scale
categorical variables

Answer 16

how are observations arranged?

nominal variables
-numerical values are used as a label or type of attributes
-no intrinsic order between categories
-zb gender, party affiliation: spö=1, övp=2

ordinal variables:
-variables of two ore more catagories which can be ranked
-value and gap is not interpretable
zb smart (no twice as smart)

Question 17

variables by scale
metric variables

Answer 17

interval variables
-variables have a zero value (usually without a clear meaning)
-distance between attributes has the same meaning

ratio variables
-zero means thet there is nothing of this variable left

Question 18

verwende datenset thedata.dta

Answer 18

use thedata.dta

Question 19

delete all variables and data

Answer 19

clear

Question 20

zusammenfassen eines datensets

Answer 20

describe, short
describe, simple

summarize
sum, detail

tabulate

list

codebook

Question 21

excel datenset importieren

Answer 21

import excel “…”

Question 22

var1 und var2 entfernen

Answer 22

drop var1 var2

Question 23

alle außer var3 und var4 entfernen

Answer 23

keep var 3 var4

Question 24

distribution table of a variable

Answer 24

tabulate …

missing values are not depicted, only if:
tabulate .., missing

Question 25

create a variable

Answer 25

generate

Question 26

give variable another name

Answer 26

rename

Question 27

change tha value of a variable for another value

Answer 27

replace

Question 28

add a description to a variable

Answer 28

label variable

Question 29

add a label to a variable value

Answer 29

2 steps needed: label define label values

Question 30

change order of variables in dataset

Answer 30

order

Question 31

measuring unobservables

Answer 31

conceptualization and operationalization are needed -> theoretical definitions ->clarify how concept is measured by sepcifiying indicators and how informaiton is gathered -> systematized good operationalizaiton is linked to your theory zb concept: study success -> attributes : academic achievement // acquired abilites -> components: received prives, amount of prize money // ability to solve problems etc

Question 32

issues of conceptualization

Answer 32

problem of conflation -sub components should be conceptually in line with attributes at the corresponding upper level -> sub-components should not relate to conceptuall different attributes problem of redundancy: -components at the same level should be mutually exclusive

Question 33

minimalist definition of attributes

Answer 33

+ availability of data may be enhanced + no redundancy with other attributes -every case is an instance, no variation -meausre might not reflect the concept well (invalidity) -measure may only be applicable for one situation

Question 34

maximalist definition of attributes

Answer 34

= including too many (irrelevant) attributes potential drawacks of overburdening: -lower usefulness as concept has no empirical referents -tautological and of little analytical use if main dependet variable is already included as an attribute

Question 35

Median

Answer 35

50% -> Wert der Mitte, value located directly in the center of collected data herausfinden: sum var1, detail -> wert bei 50%

Question 36

not normally distributed data

Answer 36

sum var1, detail skewness: positive value indivates that a variable is skweded to the right (outliers) -> if highly skewed to the right, median might be more representative that the mean, because mean is affected by outliers

Question 37

boxplot interpretieren

Answer 37

well suited for ordinal and metric data whisker from minimum value of the sample -> lower quartile (one quarter of the sample lies here) then box with median in the middle whisker showing the upper quartile whiskers are without potential outliers

Question 38

Modus

Answer 38

Modalwert = most common value of a variable

Question 39

mean

Answer 39

arithemtishces mittel average value of a variable

Question 40

bivariate descriptive statistics

Answer 40

shows the relationship between two variables options: -crosstables -comparioson of key measures zb mean -graphical comparison -correlational measures

Question 41

correlation vs causality

Answer 41

correlation: var A and var B are correlated if higher/lower values of variable A coincide with higher/lower values in variable B -> you dont know whter varA influences var B or vice versa negative correlation: if values of var A are lower, values of var B are lower as well positive correlation: if values of var A are higher, values of var B are higher as well causality: direct relationship between var A and var B -> a change in var A leads to a change in var B --> more difficult to determine, needs research design

Question 42

how can data be visualized

Answer 42

amounts zb bar charts, dots, grouped bars distributions zb histogram, boxplots proportions zb pie chart, bars x-y relationships zb scatterplot uncertainty zb error bars, geospatial data map

Question 43

sort bars in stata descending

Answer 43

graph hbar, over (var1, sort (1) descending)

Question 44

adjust bandwith of histogram

Answer 44

hist var1, width (5) vs hist war, width (10) -> more values in one bar of the histogram

Question 45

increase x and y title size

Answer 45

xtitle (, size (large)) ytitle (, size large))

Question 46

commands for tables

Answer 46

tabulate fre

Question 47

color schemes

Answer 47

...., scheme (schemename) assign individual colors bar(1, color ("black"))

Question 48

different types of inferences

Answer 48

descriptive interference: -historical accurafy of scientific information -simply observing sample data statistical inferences -use sample properties to infer properties of a populations -unterstand development ofer time or relationship between variables -focus on understand how uncertain findings are -> t-tests causal inferences: -infer the existence of a causal effect from data analysis

Question 49

hypothesis testing in stata

Answer 49

goal: infer from the sample to the population problem: population is usually unknown and only one (not infinetly many) samples are available need: an estimation of the uncertainty resulting from the use of random sampling solution: mean/standard deviation or proportion value in the sample as an estimate

Question 50

stratum

Answer 50

a subset of elements from the population that share a characteristic (usually sociodemographic zb age, gender)

Question 51

sampling frame

Answer 51

a list of elements in a population that can be identified

Question 52

convenience sample

Answer 52

-use of information from participants who are convenient to access -sampling method does not need to select participants based on any set of criteria -only use this method if representativeness is not of importance for research

Question 53

quota sample

Answer 53

is primarily used when information is to be collected on a specific, definable target opoulation -if it worked well, quota sample privides a structurally identical representation of the population -volunteers could still bias the picture

Question 54

stratified sample

Answer 54

stratified sampling involves random selection within predefined groups (e.g. gender, age) -> people within a stratum are randomly selected -strata is supposed to ensure that the make-up of the population is adequately mirrored

Question 55

simple random sample

Answer 55

selection process takes place randomly -each participant has a chance of being selected

Question 56

survey weights

Answer 56

-when sample deviates from the actual population -suvery weights are estimated variables -> even out the differences between sample and population