Pages: 10

Oslo is the capital of Norway. Like many such capitals, Oslo is made up of several city districts. Table 1.1 contains some quantitative information on three of these districts. I have named the districts A, B, and C because Norwegian names are unfamiliar and more than a mouthful to pronounce for non-Norwegians.

Pages: 46

This chapter deals with descriptive research questions or descriptive statistics. It introduces Stata and SPSS using the COVID-19 data. The chapter elaborates on the topic of what is typical for a variable, which in statistics lingo translates into three measures of central tendency: mean, median, and mode. It takes on variables’ measurement levels and explains ordinal variables as a special case regarding variables’ measurement levels for presentational ease. The chapter reintroduces commando-style statistical analysis by means of do-files in Stata and syntax-files in SPSS to obtain reproducible results. Because lifestyle choices that cause trouble later in life (e.g., high blood pressure, overweight, substance abuse) often commence during adolescence and early adulthood, the health behaviors of students have gotten a lot of attention from researchers worldwide.

Pages: 36

Variable associations are the bread and butter of quantitative research in the social and behavioral sciences. In this respect, we typically have some theoretical reason for expecting that one variable, x, affects another variable, y, in some way. 1 We then use a statistics program to examine our beliefs. I repeat: Our beliefs about how x is associated with y make us scrutinize this relationship empirically. For this reason, Chapter 2 was mainly an appetizer to the main event of statistical analysis in research settings: the examination of statistical associations. Now, let the real games begin! 2

Pages: 56

This chapter introduces multiple regression analysis. Section 4.2 begins where Section 3.6 left off; we examine the association between x 1 and y while simultaneously controlling for a second and a third x-variable, x 2, and x 3. Section 4.3 then formalizes the classic multiple linear regression equation and discusses the linear regression model per se.

Pages: 47

Chapters 2–4 concerned only what happened inside our data – as expressed perhaps a bit cryptically at the end of Sections 2.1, 3.1, and 4.1. This chapter spins around one key question: How can we be sure what we find inside our data is something that also takes place outside our data? This is an inferential research question. To answer it properly, I take a rather circumstantial route with many stops along the way. Why bother with this? The short answer is that it needs to be done!

Pages: 53

Things seldom go as smoothly as they have done in this book in real-life statistical analysis. The reason is obvious: I made the required data preparation in advance. To shed more light on such preparation, Sections 6.2–6.4 handle various aspects of statistical analysis having mostly to do with data groundwork before taking on the later descriptive, associational, and inferential analyses. Since doing data preparation is the main purpose in these three sections, I include the statistical commands in the main text.