I wrote basically everything (lecture notes, general tips from tutorial, my own tips in approaching the questions, SPSS guidelines etc.) in this document and this document had well prepared me for both my SPSS test (8.6) and exam(9.1). I don't have time to make it look nicely but it is clearly str...
**p value= sig value in spss, p-value lower than critical value, reject H0
Obtained> critical -> reject H0
Obtained increase -> less probable H0 -> lower p-value
Week 1 Web lecture
Statistics
• Univariate: measure one property from a person (e.g. What was the average grade of
the ISA exam last year?)
• Bivariate: take two properties and do relationship from one and another (e.g. Did
males and females differ in their grades?)
• multivariate: different variable related to different variable (e.g. Was the grade
dependent on initial motivation, the time spent on reading and gender?)
• The study of how we describe and make inferences from data (Sirkin)
• An inference is “a conclusion reached on the basis of evidence and reasoning.”
• Distinction between descriptive & inferential statistics
• Descriptive: measurement do on the sample, describing the data
• Inferential: taking a sample of the population and make a larger statement of the
whole population
Units of analysis & variables
• Unit of analysis: “the what or who that is being studied e.g. individual, families,
countries, companies
• Variable: a measured property of each of the units of analysis e.g. Age, household
income annual revenue
Level of measurement
• Nominal: group classification, no meaningful ranking possible, numerical coding
arbitrary
• Ordinal: meaningful ranking, unknown distance between categories
• Interval: meaningful ranking with equal distance e.g. marks from 0-10, 0 doesn’t
mean the absence
• Ratio: meaningful ranking with equal distance, absolute and meaningful zero point
e.g. age 0 = no age, 0 means absense
• ****for likert scale, can treat it as interval (1=not addicted at all ,10= heavily
addicted)
Unit of analysis and continuous or discrete variable
• A continuous variable is measured along a continuum, whereas a discrete variable is
measured in whole units or categories.” (Privitera)
, • Continuous: numbers can have decimal points e.g. a person’s heigh, surface area of a
country, average of children per woman in a country (UoA: country not woman as
they are comparing between different countries)
• Discrete: whole integer e.g. a person’s no of children, no of doctors in country (UoA:
country)
Measures of central tendency
• To univariately describe the distribution of variables on different levels of
measurement
1. Mean (interval/ratio variables)
• = all values are added up and divided by n, i.e. the number of observations in
the sample
• most useful for describing (more or less) normally distributed variable
• M (alternative notation: x)
• Questions like “what is the average trust in the news media in this sample”
• Some characteristics of mean:
o Changing any score will change mean
o Adding or removing a score will change mean (unless that score is already
equal to mean)
o Adding, subtracting, multiplying, dividing each score by a given value
causes the mean to change accordingly
o Sum of differences from the mean is zero: data-mean
å(x - M ) = 0
bb’s explanation:
Sum of all data=mean x number of data
For example 1,2,3,4,5 Mean=3
Sum of difference: (1-3)+(2-3)+(3-3)+(4-3)+(5-3)
Can rearrange as 1+2+3+4+5-3-3-3-3-3
=sum of all data- mean x number of data
=0
, o Sum of squared differences from the mean is minimal : (data-mean)²
➢ larger sum of square means that scores deviate more from the mean
➢ minimal: if we use other value other than the mean to calculate SS ->
>minimal
2. Median (ordinal & interval/ratio variables)
• = the value of the “middle case“
• Can be used for ordinal & interval/ratio variables that have skewed
distributions
In frequency tables in SPSS: first identiy the first category that exceed 50% in
the ‘cumulative percent’ column
3. Mode (Nominal & ordinal & interval/ratio variables)
• = the mode is the category with the largest amount of cases
• In frequency tables in SPSS: first identiy the first category that exceed 50% in
the ‘cumulative percent’ column
Measures of Central Tendency and normal/skewed distributions
Practical SPSS stuff
-Group size: included
-Specific variable’s data: Data > select cases
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