Hello and welcome to SPSS in 15 minutes. My name is Alexander. The goal of this video is to get you up and running with SPSS to start using it in just under 15 minutes.
In this video, I will tackle defining variables, entering data, and analyzing your data using descriptive statistics such as frequencies and other summary statistics. So, I think we should get started right away. Right. So I've opened SPSS, you see that the interface may look similar if you have ever used Microsoft Excel, it looks like a spreadsheet.
This spreadsheet is divided into two. There is the data view here, which is where you see your actual data, and there is the variable view, which is where you actually define and edit the questions that you've been asking on your survey or whatever questionnaire that you have. The first thing that you do when you come to SPSS if you don't have data is obviously to bring data in.
So what I will do is I'll show you. you how you can create variables and enter data. Right. So I have a little questionnaire that I'll be using.
The first variable I have on my questionnaire is interview ID. The rule of the variable names is such that you need to not include any spaces in the name and no symbols and or any special characters. Okay.
So I'll just say interview ID. The type of the variable. variable I'll just click the button here is basically how in which format is the variable is the data of that variable going to come in and for interview ID it's obviously gonna be numeric it's basically just numbers I'll just go ahead and click OK all right the width is just the maximum number of characters that would be allowed for this variable when I'm entering data for this variable 8 is quite generous so I'm just gonna leave it like that decimals as you have suggested it it actually it's actually how many decimal places this variable is gonna have.
For interview IDs, no decimal places. I'll just remove this so that it's zero. Right, I'm using the Tab key on my keyboard to move to the next.
So the label is how the name of this variable should actually appear when I'm doing analysis, of which I'm just gonna say interview with a space now ID. The values are the list of responses that are allowed for that question. This is mostly for questions that are multiple choice, which the interview ID is not going to be multiple choice. Missing is basically values that you are using to refer to missing values like not applicable or if the question was not answered. We're not going to set this in this video.
Columns is basically just how wide the column for this data. is for this variable is going to be when you go to the data view. Alignment is just whether the data should be on left, right or center. Measurement level is basically the type of the variable that you're dealing with as regards to how you are measuring it.
To make this easily understood, I would just say that scale variables are variables that are talking about quantities. These are genuinely quantitative variables like how many people are there in your household. You turn them on. about the number of people which is obviously a quantity or how old are you you're talking about the quantity of time or how long is it from here to the next boho which is the quantity of distance length and so on and so forth that would be scale variables ordinal variables are variables that are represented by words but you can say that one category or one value is obviously on top of one another value in terms of in terms of quantity or in terms of quality for example if you think about education levels somebody who is at secondary school obviously has more education than one someone who is at primary school or someone who is a tertiary school has more education than someone who is at secondary school which means the variable education level should definitely be an ordinal variable while nominal variable basically this is quite quite similar to the ordinal variable in that it's represented by words, but then you may not actually be able to say that one category or one value has more of the variable than another value in that variable.
So for example, gender. Gender, we cannot say that male has more gender or more sex than female. So this variable obviously is not ordinal, but rather it's nominal.
Nominal variables, examples would be race or tribe or religion. affiliation we cannot say that Christianity or being Christian is more religion than Islam they're just names of religions that's where nominal is coming from so for interview ID it's neither scale or ordinary because these are just random numbers we are assigning to people so it's gonna be nominal let's go to the next one the next one is gonna be the name of this person so name now name cannot be numeric obviously people we have to type as text So you click the button and you're going to select string. String means text variables. Go ahead and click okay.
And then you go ahead to insert the label, which is just going to be name. I want to have any values? No. I may not be able to possibly know all the names so that I can fill them beforehand.
So this is just going to be an open ended question. No values, no multiple choice whatsoever. Cost of string variables.
are nominal so I just leave nominal as a default the next variable is gender now gender is going to be numeric variable which begs the question why gender you have male and female which are words yes but then we don't want people to have problems entering the data missing spelling and so on this verb is very important so what we do is we specify values so we have numbers that you represent the values of gender which are words so for example say one is going to be standing for male and two stand for female before we get there decimals i'll change this to zero the label is going to be gender then we go to values click the button and the value one is going to stand for male then you click add the value two is going to stand for female and they'll click add and that's enough you click OK that's how you set the values or match for trace of a variable in SPSS right then we go to measurement level if you remember I just mentioned that gender is a nominal variable because you cannot say that male has more gender than female the next variable is gonna be age age is traditionally in numerical variable it's quantitative you may want it to have decimals or leave it like that the label I'll just say H we're not going to put values because it's gonna be open-ended if you're 15 years old you say 15 if you're 10 years old you said 10 if you're 50 years old you said 50 the measurement level since this variable has a unit of measurement in years and it's obviously numerical quantitative it's talking about quantity of time then it's going to be scale the last variable is a question did you eat rice in the past seven days so to make the name easier to read I'll just say rice the type is going to be numeric why because the response says yes and no I'm just going to quote them or give them value so zero is going to stand for no and one for yes no decimals right no I have to type the whole question this time so did you eat rice in the past seven days I have to set the values so I click the button the values will be zero is no and one is yes click add and click ok so we may debate here on the measurement level is this a scale Sorry, is this an ordinal variable or a nominal? It cannot be scaled because obviously you're talking about no and yes, which are words. But is it nominal?
Can we say that yes is more than no? Yes, it is. or is it nominal so for this variable i'm just gonna put it at ordinal once you do that it means we have defined all the variables the next thing is to enter data the way you have to switch data view to enter the data now the way that we enter data is actually very straightforward is basically how you enter data in microsoft excel but before we go there i would say that go ahead and click this button here which says value labels that allows us to see especially when the variable has values so if we type one for male it should actually show us male instead of showing us one so you turn that on then we'll go ahead and start entering data So now that we have all the data in, the next step is now to analyze our data. Okay, so to analyze your data, what you're going to do is go to analyze, descriptive statistics.
What's that value? at frequencies. Frequencies are just counts or a matter of how many of this value do we have. This works much better for variables that don't have too many values. For example, For age, we have too many values of age, too many different values of age.
While on gender, we only have a few values of gender. And did you eat rice in the past seven days? We actually also do have just two values.
So when you are running frequencies, I would very much recommend that you run frequencies for variables that have a few categories, like gender and did you eat rice in the past seven days. So what I would do is I would drag the variable to the right-hand side. I'll drag the other one to the right-hand side as well.
The next thing is I want to throw in a few options. I'll go to charts and say I want some bar charts, okay, with frequencies, not percentages, and I'll go ahead and click continue. Make sure that we are displaying frequency tables, especially if the variables are categorical or they are either nominal or ordinal. go ahead and click OK and we have our analysis agenda there are Wow 10 males and 10 females it's interesting and then did you eat rice in the past seven days we had eight people who did not eat rice and 12 people actually eat rice this is the charts for males I've solved sorry for gender and this is chart for rice if you want to get this chart or the table in another program like Microsoft Word, all you have to do is right click and copy.
In Microsoft Word, you just go ahead and paste. And then you write whatever narrative you want to write. Let's go back to your species. How about a variable like age? All right.
Okay. Go to analyze descriptive statistics. We'll go back to frequencies. This time, I will reset this.
Okay. Click reset. button and then I'll throw in the variable h which is a scale variable or continuous variable now for continuous variables you may not want to use frequencies because you there's a hyperbola b that you have so many different values of this variable what you want to do is to probably just go to statistics you and say you want probably the mean the median maybe you also want the standard deviation and the range maybe the minimum and the maximum you may also want the quartiles and click continue when it comes to charts You probably want to have the histogram, which is much the graph that you may want to actually run for variables that are continuous like age. And you want to show the normal curve on the histogram and click continue. The next thing that you want to do is you want to show the curve of the graph.
So you want to show the curve of What you should not forget is to turn off display frequency tables because again, you have too many values, different values for this variable. So the table which shows all the values and how many people there are on each of the values, that is not going to work very well. So turn off this and you click okay and now you will see that we have a table of statistics which show us that the average age is 20 years old but the median age is 21 which is not is quite close. The standard deviation for age is 3.5, and it's the age ranged from a minimum of 13 and a maximum of 28. The first, at the top of the first 25 percent people, the is an 18 year old, and the median, which is also known as a 50th percentile, is 21. And at 75 percent of the distribution, you have have someone who is 22.75 is all.
And we have our histogram, which shows us that our data is roughly normally distributed with more people at the center, which is at the median of 21. And it's basically coming down here and coming down. So this is a very good variable if you want to go ahead and do other advanced analysis. If you want more learning, check out our YouTube channel or our website uniquemartymedia.net forward slash learning but for me at this point it's been under 15 minutes thank you