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Digital insights 3 - Coggle Diagram

- - - - We assume that the null hypothesis (H0) is true (there is no effect, or the effect that you may observe happens by chance)
      - We fit a statistical model to our data – a test statistic – that represents the AH (H1) and see how well it fits (how much variation in scores does the test statistic explain versus how much is due to chance)
      - To determine how well the model fits the data, we calculate the probability of getting that model if the NH (H0) were true (called the p-value) (= the probability that the effect is due to chance)
      - If the p-value is very small (.05 or less), we can conclude that the model fits the data well and gain confidence in the alternative hypothesis → H1 is likely to be true: there is a high probability that the effect we observe does not happen by chance
- - - - Crosstab
        
        Relationship between 2 categorical var.
      - Chi2 Test
        
        Stat. sign. diff between the obs. freq. & the expected freq.
  - - - How many samples to compare ?
        
        1 sample
        
        Comparison of a mean to a norm
        
        2 independent samples
        
        Independent sample T-test
        
        2 dependent samples
        
        Paired sample T-test
        
        .> 2 independent samples
        
        One-factor ANOVA
- - - - ex: Is a woman pregnant or not pregnant ?; The political party people vote for: party A, B, C or no vote ?; Students who pass or fail the Data Analysis class
    - - ex: A woman is either pregnant or not pregnant (cannot be ‘a bit pregnant’); For which political party people vote ? (a person can only vote for 1 party or another, or decide not to vote)
    - - IV = 1 sample
        
        Describe the sample => determine the nbr of resp (freq./ %) associated to the diff. cat. of the outcome var.
        
        ex: For which party do Americans vote: % republicans; % democrats ?; Which fruit do French people eat the most: % apples; % oranges, etc.?
        
        Frequency table
        
        SPSS > Analyses > Descriptives statistics > Frequencies
      - IV = more than 1 sample
        
        Several nominal var. to analyze together (ex: gender/educational lev.; age/internet usage)
        
        We can combine cat. of the diff nominal var. to make statistical inferences about the pop.
        
        Contingency table = crosstabs
        
        2 cat. var. (1 IV & 1 DV)
        
        Test relationship or association
        
        ex: 2 nominal variables (gender & internet usage) with the samples Internet usage (heavy versus moderate) and gender (men, women)
        
        We expect Internet usage to be related to gender: Heavy Internet users are mostly men
        
        Chi-Square test of Independence
        
        = tests whether 2 cat. var. forming a contingency table are associated
        
        it compares the frequencies observed in certain categories to the frequencies you might expect to get in those categories by chance
        
        (H0: The 2 cat. var. are indep. of each other (the frequencies observed in the diff. cat. are due to chance; H1: There is a systematic association between the variables)