element to point out in this crosstab was the diﬀerence between female students with GPA above 3.0 and male students with GPA above 3.0. Being this diﬀerence above 10%, we can assume there is a signiﬁcant relation between grade and gender. 65.1% - 50% = 15.1%
is a variable which is held constant throughout a research in order to assess the relationship between dependent and independent variables. Since it remains constant, it enables researchers to test and better understand the relationship between dependent and independent variables.
WITH CONTROL VARIABLE IN SPSS ▪ Go to Canvas ◦ Assignments > SPSS Lab 7 - Bivariate with control variable and selecting cases ◦ Download the Week-7.sav ﬁle ▪ Open the ﬁle on SPSS ◦ Launch SPP ▪ File > Open > Data… ▪ Find and open the Week-7.sav ﬁle
A HYPOTHESIS People who spend more time on Instagram will tend to have more followers than people who spend less time on it. Null hypothesis: there is no relationship between time spent on Instagram and number of followers
time spent on Instagram (independent variable) Number of followers (dependent variable) Example of a direct relation. If average time on IG increases, followers also increase.
“cut” scale variables in order to identify two ranges to use to deﬁne the values of the new variable? A good idea is to use a measure of centrality (mean, median or mode) as a threshold value to identify the two ranges. For example: 1. Right click on the variable name. Select :Descriptive statistics 2. Mean or Median can be used as threshold values.
for identifying a threshold is to refer to arbitrary values that are meaningful in the context of the analysis. For example, in the case of this hypothesis, we can deﬁne people who use Instagram less than 1 hour a day as casual users, and people who use it for more than one hour a day as intense users. All arbitrary values are good threshold values as long as they are consistent with your hypothesis.
1. Add the variable you want to use as a ﬁlter 2. Specify the conditions for inclusion. Separate multiple conditions with the command “or” or “And”. Pay attention to quotes, spaces, capitalizations. IG_followers_new = 'More followers' or IG_followers_new = 'Less followers'
The missing values column is gone % are diﬀerent compared to the initial bivariate analysis Important: Make sure to reintroduce all your cases before performing any other analysis on diﬀerent variables. To do so go to Data > Select Cases and click on “All cases”
between % is less than 10%, we can conclude that the data do not support our the hypothesis. The Null hypothesis is true: there is no relation between independent and dependent variable.
happens when we add a control variable? Let’s try to add “Gender” as a control variable. To do so, go to Analyze > Descriptive statistics > Crosstab Independent variable in the Rows Dependent variable in the Columns Control variable in the Layer
VARIABLE Male students who spend less time on Instagram tend to have more followers. Inverse relation between time on Instagram and followers Zero order relation. No signiﬁcant relation between time on instagram and number of followers
VARIABLE Zero order relation. No signiﬁcant relation between time on instagram and number of followers Female students who spend more time on Instagram tend to have more followers. Direct relation between time on Instagram and followers
variable revealed inverse and direct signiﬁcant relations in the partials, while the zero order relation conﬁrmed the null hypothesis (no relation between time on Instagram and number of followers). Therefore, we can conclude that the Gender is a suppressor variable (see Prof.Al-Rawi Week 8 videos for the kind of relations between independent, dependent and control variables).
begin by commenting the zero order relation. Is there a signiﬁcant (more than 10%) relation? Is it direct or inverse? Then analyze each partial. 1. Is the relation’s direction (direct or inverse) in each partial the same as the zero sum relation? 2. Is it more or less intense than the zero sum relation? (meaning, is the diﬀerence in % greater or lower). 3. Is it signiﬁcant? (more than than 10%) Lastly, try to map the variables using the models described in Prof.Al-Rawi Week 8 lecture? (speciﬁcation, interpretation, explanation, replication, suppressor variable, distorter variable).
done in class: independent variable: time on Instagram. Dependent variable: number of followers. Instead of using Gender as a control variable, try to use Canadian_birth. Does the result change? Upload the crosstab with the control variable and comment on the partial relations. Upload a screenshot of the crosstab on Canvas alongside a one sentence comment about the relation (or lack thereof) between the two variables.