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Calculating inferential statistics for a single mean

Video: Calculating inferential statistics for a single mean

SPSS makes it very easy for you to go beyond your sample data and make inferences about the population that those data came from, that is, you can calculate inferential statistics. In the last movie, we looked at how to work with proportions for a single dichotomous variable-- that's a yes/no, 0/1 variable-- to get a hypothesis test and a confidence interval. In this movie, we will do the same procedure for a scale variable, something that could be measured in set units, like time to complete a project or bids from vendors. I am going to use the same data set for this one, the GSS, or General Social Survey.sav, data set, and this time I'll be looking at the one variable here that's called FamilyIncome that measures the total family income in dollars.

Calculating inferential statistics for a single mean

SPSS makes it very easy for you to go beyond your sample data and make inferences about the population that those data came from, that is, you can calculate inferential statistics. In the last movie, we looked at how to work with proportions for a single dichotomous variable-- that's a yes/no, 0/1 variable-- to get a hypothesis test and a confidence interval. In this movie, we will do the same procedure for a scale variable, something that could be measured in set units, like time to complete a project or bids from vendors. I am going to use the same data set for this one, the GSS, or General Social Survey.sav, data set, and this time I'll be looking at the one variable here that's called FamilyIncome that measures the total family income in dollars.

Now I should point out that these are actually the midpoints for categories, which is why they seem to be very precise amounts, and you will see them repeated, like here's 115,841, and here's the same number again. Nevertheless, these are scale variables because the dollars move in set amounts. So I am going to be doing a hypothesis test and a confidence interval for the family income for the 349 people in this particular sample. Now there's two ways to do this, and both of them go in the Analyze menu.

For the first one, I am going to come up to Analyze and I am going to go Compare Means and I am going to use what's called the One-Sample T-Test. And all I need to do here is I need to pick the variable that I want. In that case, it's FamilyIncome. So I just double-click on that and it moves it over. Let's look at some of the options. I can get a confidence interval, and I can change it from 95% to some other values, sometimes 90% or 80% is appropriate, but 95% is the most common. So I am going to leave it right there.

So I'll click Continue. I'm going to ignore the bootstrap, because that's there because of an extra add-in that's installed in this version of SPSS that normally you have to pay for. Below the test variables box, I have another box that says test value, and this is the value that SPSS is going to compare the mean family income to, to find out if it's significantly different from it. Now I can guarantee you that the mean family income is not going to be 0, so I am going to pick another number to put there. Let's say, for instance, I want to compare it to \$45,000 for family income.

This is how I can do it to find out whether this average value is higher or lower than that significantly. So now I click OK, and what I have is one sample of statistics. It tells me that I have 349 people, that the mean family income is \$32,781 with a standard deviation of 29,000. The last one, the standard error, is used in calculating the hypothesis test and the confidence intervals. Below that I have what's called a One- Sample Test where SPSS is taking the average value, the mean of 32,781, and comparing it to a hypothesized value of \$45,000.

The first column has what's called the t statistics, and that's an inferential statistic, and it doesn't necessarily mean a lot on its own. The second one is the degrees of freedom, which has to do with the sample size. It's the third one in particular that we want to look at. It says Sig. (2-tailed). That's the significant value, or the probability value for the hypothesis test. And in this case that number is 000. Now it's not literally 0. it's just it's less than 001, so it shows up truncated here. What this tells me is that the observed average value of \$32,781 per year for a family is significantly different from my hypothesized value of 45,000.

I was optimistic in my hypothesis. Now these last two columns have what's called confidence interval for the difference from the mean. You see that the mean difference that's in the third column from the end is -12,000. That's because the observed value is about \$12,000 less than my hypothesized value. These last two columns give me the confidence interval for that difference. Now an interesting thing here is had the hypothesized value been 0, these would have been an actual conference interval for the mean, but because I felt that having 0 would be a silly test value, I put something else in. The confidence interval is for the difference.

Now if I want a regular confidence interval, a better way to get that, instead of from the T-Test, is to go back to a procedure we looked at in the last set of the videos, the Explore command. I just go back up to Analyze > Descriptive Statistics > Explore. I take the one variable that I want out of this list, which is FamilyIncome, and I put it into the Dependent List, that means outcome variables, or the ones we are trying to chart. All I want here is a list of statistics. I am going to come down to Display and click on Statistics and press OK.

I'm going to get a big table here, but the only one I really want to look at is this one that says 95% confidence interval for the mean, with the lower bound and the upper bound. There's actually several ways of interpreting a confidence interval, but one sort of colloquial way is to say that the population value is between 29,692 and 35,871, so between 30,000 and 36,000. There is about 95% chance that the true population mean is between those two values. Anyhow, SPSS makes it simple to perform two of the most basic and two of the most useful inferential statistics for a single scale variable: the One-Sample T-Test and the simple confidence interval.

In the next movie, we will look at something slightly more complicated as we look at the distribution of cases across a nominal variable with several groups.

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This video is part of

SPSS Statistics Essential Training (2011)

52 video lessons · 20106 viewers

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1. Introduction

2m 58s
1. Welcome
1m 5s
2. Using the exercise files
40s
3. Using a different version of the software
1m 13s
2. 1. Getting Started

19m 0s
1. Taking a first look at the interface
11m 49s
2. Reading data from a spreadsheet
7m 11s
3. 2. Charts for One Variable

21m 54s
1. Creating bar charts for categorical variables
7m 18s
2. Creating pie charts for categorical variables
2m 54s
3. Creating histograms for quantitative variables
5m 45s
4. Creating box plots for quantitative variables
5m 57s
4. 3. Modifying Data

33m 10s
1. Recoding variables
5m 33s
2. Recoding with visual binning
5m 33s
3. Recoding by ranking cases
5m 26s
4. Computing new variables
5m 37s
5. Combining or excluding outliers
5m 21s
6. Transforming outliers
5m 40s
5. 4. Working with the Data File

28m 12s
1. Selecting cases
6m 44s
2. Using the Split File command
5m 12s
3. Merging files
5m 33s
4. Using the Multiple Response command
10m 43s
6. 5. Descriptive Statistics for One Variable

22m 14s
1. Calculating frequencies
8m 43s
2. Calculating descriptives
5m 31s
3. Using the Explore command
8m 0s
7. 6. Inferential Statistics for One Variable

16m 3s
1. Calculating inferential statistics for a single proportion
6m 6s
2. Calculating inferential statistics for a single mean
5m 39s
3. Calculating inferential statistics for a single categorical variable
4m 18s
8. 7. Charts for Two Variables

30m 43s
1. Creating clustered bar charts
7m 10s
2. Creating scatterplots
5m 8s
3. Creating time series
3m 24s
4. Creating simple bar charts of group means
4m 17s
5. Creating population pyramids
3m 0s
6. Creating simple boxplots for groups
3m 3s
7. Creating side-by-side boxplots
4m 41s
9. 8. Descriptive and Inferential Statistics for Two Variables

45m 28s
1. Calculating correlations
8m 17s
2. Computing a bivariate regression
6m 27s
3. Creating crosstabs for categorical variables
6m 34s
4. Comparing means with the Means procedure
6m 33s
5. Comparing means with the t-test
6m 4s
6. Comparing means with a one-way ANOVA
6m 30s
7. Comparing paired means
5m 3s
10. 9. Charts for Three or More Variables

24m 30s
1. Creating clustered bar charts for frequencies
6m 34s
2. Creating clustered bar charts for means
3m 45s
3. Creating scatterplots by group
4m 13s
4. Creating 3-D scatterplots
4m 25s
5. Creating scatterplot matrices
5m 33s
11. 10. Descriptive Statistics for Three or More Variables

30m 57s
1. Using Automatic Linear Models
11m 52s
2. Calculating multiple regression
9m 3s
3. Comparing means with a two-factor ANOVA
10m 2s
12. 11. Formatting and Exporting Tables and Charts

29m 29s
1. Formatting descriptive statistics
6m 1s
2. Formatting correlations
7m 49s
3. Formatting regression
10m 19s
4. Exporting charts and tables
5m 20s
13. Conclusion

51s
1. What's next
51s

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