100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
logo-home
Previously searched by you
Previously searched by you
logo
Summary Discovering Statistics Using IBM SPSS - Quantitative Research Methodology (MAN-BPRA247) CA$9.89   Add to cart
Quickly navigate to
Preview
  2.  
  3. Radboud Universiteit Nijmegen (RU)
  4.  
  5. Kwantitatief Onderzoek (MANBPRA247)

Summary

Summary Discovering Statistics Using IBM SPSS - Quantitative Research Methodology (MAN-BPRA247)
 26 views  0 purchase
  • Course
  • Kwantitatief Onderzoek (MANBPRA247)
  • Institution
  • Radboud Universiteit Nijmegen (RU)
  • Book
  • Discovering Statistics Using IBM SPSS

This summary contains everything you need to know for the course 'Kwantitatieve Onderzoeksmethodologie' (in Dutch) at Radboud. Note: If you want more chapters of Field's book, I have a bundle containing two summaries, each with different chapters summarised.

Preview 3 out of 24  pages

Document information

  • No
  • Chapter 2, 6, 9, 11, 12, 13, 14, 17, 18, 19, and 20
  • October 12, 2023
  • 24
  • 2021/2022
  • Summary

Subjects

  • statistics
  • quantitative
  • research
  • spss
  • regression
  • linear
  • factorial design
  • anova
  • manova
  • factor analysis
  • logistic regression
  • linear regression

Connected book

book image

Book Title:

Author(s):

  • Edition:
  • ISBN:
  • Edition:

More summaries for

  • 2324 Statistics MAN-MOR004 2024 ALL VIDEOS
  • 2324 Statistics MAN-MOR004 2024 ALL VIDEOS
  • Summary Discovering Statistics Using IBM SPSS Statistics
All for this textbook (8)

Written for

  • Radboud Universiteit Nijmegen (RU)
  • Bedrijfskunde
  • Kwantitatief Onderzoek (MANBPRA247)
All documents for this subject (8)

Seller

avatar-seller
Mirthe023

Content preview

Summary Kwantitatieve
Onderzoeksmethodologie


Discovering Statistics Using IBM SPSS Statistics
Andy Field




Content
Chapter 2. The Spine of Statistics...........................................................................................................2
Chapter 6. The Beast of Bias...................................................................................................................4
Chapter 9. The Linear Model (Regression).............................................................................................6
Chapter 11. Moderation, Mediation and Multicategory Predictors.....................................................12
Chapter 12. GLM 1: Comparing Several Independent Means...............................................................14
Chapter 13. GLM 2: Comparing Means Adjusted for Other Predictors (Analysis of Covariance)..........14
Chapter 14. GLM 3: Factorial Designs...................................................................................................15
Chapter 17. Multivariate Analysis of Variance (MANOVA)...................................................................16
.............................................................................................................................................................16
Chapter 18. Exploratory Factor Analysis...............................................................................................17
Chapter 19. Categorical Outcomes: Chi-Square and Loglinear Analysis...............................................21
Chapter 20. Categorical Outcomes: Logistic Regression.......................................................................23




1

,Chapter 2. The Spine of Statistics
The SPINE of statistics stands for:
Standard error
Parameters
Interval estimates (confidence intervals)
Null hypothesis significance testing
Estimation

Outcomei = model + errori
Models are: regression, moderation, ANOVA, multilevel model, t-test, mediation, and correlation.

Population can be very general (all human beings) or very narrow (all male ginger cats called Bob).
A sample is a smaller subset of the population from which the data is collected.

Degrees of freedom = number of scores used to compute the total adjusted for the fact that we're
trying to estimate the population value.

The sum of squared errors and the mean squared errors (variance) can be used to assess the fit of a
model.

Method of least squares or ordinary least squares (OLS) = the principle of minimizing the sum of
squared errors.

Sampling variation happens because they contain different members of the population.
Sampling distribution is the frequency distribution of sample means (or any other parameter) from
the same population.
The sampling distribution tells us whether sample means are typically representative of the population
mean.

The standard error of the mean (SE) or standard error is the standard deviation of sample means.
It measures how representative of the population a sample mean is likely to be.
 A large standard error (relative to the sample mean) means that there is a lot of variability
between the means of different samples and so the sample mean we have might not be
representative of the population mean.
 A small standard error indicates that most sample means are similar to the population mean.

Central limit theorem = when the sample is sufficiently large (n > 30), the sampling distribution will
be (approximately) a normal distribution.

The confidence interval for the mean is a range of scores constructed such that the population mean
will fall within this range in 95% of samples.
It is NOT an interval within which we are 95% confident that the population mean will fall.
Example: a confidence interval of 95% means, 95% of your samples will contain the population value.

The point estimate is a single value from the sample.
An interval estimate is when we use our sample value as the midpoint but set a lower and upper limit
as well.
Lower boundary of confidence interval = mean - (z-score interval * standard error)
Upper boundary of confidence interval = mean + (z-score interval * standard error)

Small samples have a t-distribution.
t-distribution = a family of probability distributions that change shape as the sample size gets bigger.



2

, Null hypothesis significance testing (NHST) is a method for assessing scientific theories. The basic
idea is that we have two competing hypotheses:
1. Alternative hypothesis: an effect exists (also called experimental hypothesis).
Example: if you imagine eating chocolate you will eat less of it.
2. Null hypothesis: an effect doesn't exist.
Example: if you imagine eating chocolate you will eat the same amount as normal.
We compute a test statistic that represents the alternative hypothesis and calculate the probability that
we would get a value as big as the one we have if the null hypothesis were true. When the probability
(p-value) is:
< 0.05 we reject the idea that there is no effect and say that we have a statistically
significant finding.
> 0.05 we do not reject the idea that there is no effect, and we say that we have a non-
significant finding.

Hypotheses can be directional or non-directional:
 Directional: an effect will occur + the direction in which it will occur. Example: more or less.
 Non-directional: an effect will occur, but no direction is mentioned.

A test statistic is a statistic for which we know how frequently different values occur.

signal variance explained by t h e model effect
Test statistic= = =
noice variance not explained by t h e model error

signal ¿ parameter effect
Test statistic= = =
noice sampling variation∈t h e parameter error

One-tailed test: a statistical model that tests a directional hypothesis.
Two-tailed test: a statistical model that tests a non-directional hypothesis.

If your one-tailed test results go the opposite way you expected, ignore them, and go with the null
hypothesis. Doing otherwise means using a different significance level in a two-tailed test.

There are two types of errors one can make when testing hypotheses:
 Type I error occurs when we believe that there is a genuine effect in our population, when in
fact there isn't.
α = probability of making a Type I error.
 Type II error occurs when we believe that there is no effect on the population when, in reality,
there is.
β = probability of making a Type II error.

Familywise or experiment-wise error rate = the probability of making a Type I error in any family
of tests when the null hypothesis is true in each case. The 'family of tests' can be loosely defined as a
set of tests conducted on the same data set and addressing the same empirical question.

Statistical power = the ability of a test to find an effect = 1 - β
It depends on:
 How big the effect is.
 How strict we are about deciding that an effect is significant.
 The sample size.




3

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller Mirthe023. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for CA$9.89. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

77764 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 14 years now

Start selling

Recently viewed by you

Exam (elaborations) ·

(0)

LSCM Final Exam 1 2024 with 100% Correct Answers

CA$9.89
  • (0)
  Add to cart