**In one sentence:**Confidence intervals around a sample parameter (e.g., mean) express the range of values that are likely to include the true population parameter.

**Step 1. Understand sampling and sample error.**

Imagine we wanted to know the mean IQ of psychology undergrads. It would be pretty impossible for us to check the IQ of every single psychology undergrad in the world. Instead we would have to draw a

**sample**. Based on our sample we will try to infer the mean intelligence of the psychology undergrad

**population**. However, our conclusions will be different depending on the sample we draw – this is called

**sampling error**. As long as we deal with samples instead of entire populations, there will always be an error.

**Step 2. Confidence intervals**

Confidence intervals characterise the uncertainty around a sample estimate. So, based on our example, instead of saying that we estimated that the mean IQ is 120, we would say that it is estimated that the mean IQ of the population of undergrad psychology student’s lies between 110 and 130. The confidence intervals tell us that our population mean lies within a specific interval. The said confidence can vary and it is pretty much up to us to choose. Most often people report 90 or 95 % confidence intervals. If we choose to calculate the 95% CI, then the CI will include the true population mean in 95 times out of a 100 cases.

**Step 3. What affects the width of our confidence intervals?**

The width of confidence intervals depends on two things:

- Population Variation How much does the variable vary in the population? Do most students have similar scores around 120 or is the population more heterogeneous, i.e., many students have very low scores while others have very high scores. If the population has low variation, then any sample we choose will have low variation as well. If, however, the variation is large then it is possible that we draw a sample that results in wider confidence intervals.
- Sample Size: How big is the sample compared to the entire population? Is our sample just 10 or 1000 students out of 100000? If our sample is small, we really won’t have much information to accurately infer the population mean. Smaller samples would differ more from each other --> resulting in wider confidence intervals. Larger samples would probably not differ too much from each other --> smaller confidence intervals. The large sample reduces sampling error and causes us to have more faith in our mean estimate!

**Step 4. How are confidence intervals calculated?**

CI for population mean can be easily calculated with the following formula

Confidence interval =

Now, what is t? – t is the value of the

*t*-distribution that we need to look up in a

*t*-distribution table. It is dependent on our choice of the confidence intervals (do we want 90%, 95% or maybe 99% confidence intervals) and the degrees of freedom. Looking at the formula, you should see that the bigger the

*t*-value, the bigger will our confidence interval be. You multiply the

*t*-value by the standard error and will get the margin of error. Now, all that’s left to do is to add and subtract the margin of error from our sample mean to get the confidence interval. Thankfully, we hardly ever will have to calculate CI by hand as SPSS does it nicely for us.

**Step 5. Interpretation**

The confidence level of for example 95% tells us that if we were to draw as many as 100 samples from the same population and calculate confidence intervals for each one, 95 of these confidence intervals would include the true population mean.

By Aleksandra Szafran (University of Dundee, Academic Year 2015-16)

**In one sentence:**Beta coefficients in a regression tell us how much the predictor influences the dependent variable.

**Step 1. What is a regression?**

A regression allows us to predict an outcome variable from one or several predictor variables. For example, we could be interested in knowing what makes a comic book movie successful. We would judge the movie success by its box office earnings. There are many factors that could influence number of people going to see such a movie, for example: movie budget and the popularity of the main superhero (on a 1-10 scale). The regression could allow us to predict the box office success from movie’s budget and the superhero’s popularity if those two do contribute to the movie’s success.

**Step 2. How to do a regression in SPSS?**

Now, let’s try to run a regression analysis in SPSS using our movie example. Since we have two predictor variables, we need to run a multiple regression. The procedure is simple. Go to Analyze --> Regression --> Linear. Now, enter our variable of interest (box office earnings) as the dependent variable and our predictors as independent Variables. Press OK! And that’s how we run a regression analysis.

Hurray! Now, let’s proceed to next step and look at our output.

**Step 3. Regression equation**

This is our output:

The output shows us that our model is statistically significant.

The regression equation is: Box Office Earnings = - 82.485 + (1.188 x Budget) + (74.187 x Super Hero Popularity) + Error

So, what does it all mean?

The fist number in green is our constant or intercept. It’s the value of y when all predictors are 0. In this case a movie is expected to lose 82.485 million if budget and SH popularity are both 0. The other two numbers (the red and blue ones) are the

**beta coefficients.**

**Step 4. Unstandardised and standardised beta coefficients.**

The beta coefficients represent the slope of the regression line. It tells us the strength and direction of the relationship between predictors and outcome variable. The b values tell us how much each predictor contributes to our model.

In our case, both our beta values are positive and significant which means that as they increase the box office earnings as movie budget and superhero popularity increase. If the values were negative, we would naturally conclude a negative relationship. Moreover, the beta values tell us to what extent one predictor influences the outcome variable, if all other things are constant.

**In our example we can say that as superhero popularity increases by 1 unit, box office earnings (in million dollars) increase by 74.187 x 1. Similarly, as Budget is increased by 1 million the box office earnings will increase by 1.188 x 1 IF superhero popularity is held constant.**

As you can see, our two beta coefficients are different: 74.187 and 1.188. You could be perhaps tempted to say that superhero popularity is a 62.45 times better predictor of movie earnings compared to movie budget ( 74.187/1.188 = 62.45).

**This is absolutely wrong!**

As you should have noticed, in our regression equation we looked at

**unstandardised beta coefficients**. What does it mean?

You should note that our units of measurement were very different. Superhero popularity scores ranged from 1 to 10. That’s a very small range, while movie budget which in real life is in millions, was expressed in hundreds ranging from 101.45 to 280.54. Much bigger numbers than 1-10. Those units of measurements affect the unstandardised beta coefficient.

But, if you look to the right of the output table, you will see the

**standardised beta coefficients**. Those, in many ways are more useful and easier to interpret. They are called standardised as they are unaffected by the unit of measurements we use. So, in our example, whether we expressed movie budget in hundreds or millions, our standardised beta coefficients would be comparable. On the other hand, our unstandardised beta coefficients could not be easily compared to each other. Standardised beta coefficients are in standard deviations (SDs), i.e. as our predictor changes by one SD, by how many SDs does the outcome variable change?

In our example the standardised coefficients are as follows:

Movie budget = .250

Super hero popularity = .748

To understand this in more detail we need to know the SD of our outcome variable and predictors. For super hero popularity the SD is 2.47, for movie budget the SD is 51.45.

The SD for box office earnings is 244.68

**Thus, the standardised beta value tells us that:**

As movie budget increases by 51.45 (one SD), box office earnings increase by .250 x 244.68 =

**61.17**As super hero popularity increases by 2.47 (one SD), box office earnings increase by 0.748 X 244.68 =

**183.02**Unlike in the case of unstandardised beta values, we can directly compare standardised ones. Therefore, in this case we can safely conclude that super hero popularity contributes more to a movie’s box office earnings compared to movie budget.

By Aleksandra Szafran (University of Dundee, Academic Year 2015-16)

**What is it?**

The Pearson’s correlation coefficient (also known as the Pearson’s product moment correlation coefficient) is a parametric test used to assess how two continuous variables co-relate in a linear manner. The standardised statistical output of the correlation coefficient is typically referred to as

*r*, which specifies the strength and direction of the relationship between two variables (Steele et al., 2012).

**Why use it?**

The correlation coefficient is useful if we want to observe the relationship between two variables that already exists in a specific population. Through this observation, when one variable (x) changes we are able to see what happens to the second variable (y). More specifically, the correlation coefficient highlights four elements of a potential relationship (Field, 2014; Steele et al., 2014).

- If a relationship exists: The linear relationship between two variables is standardised into numerical form where it is represented by
*r*(i.e. correlation coefficient), and lies on a continuum from -1 to +1. Where no linear relationship exists*r*= 0 (e.g. a change in x is not associated with a change in y) (Figure 1), however where a perfect positive linear relationship*r*= 1.0 or perfect negative linear relationship*r*= -1.0 exists (e.g., as x changes, y changes by the same amount). - If the relationship is positive or negative: When a positive relationship exists an increase in x is associated with an increase in y (i.e. the value of r is greater than zero) (Figure 2), however when a negative relationship exists an increase in x is associated with a decrease in y (i.e. the value of r is less than zero) (Figure 3).
- The strength of the relationship: The magnitude of the relationship is demonstrated by the effect size. A small effect size occurs when
*r*= 0.1, a medium effect size when*r*= 0.3, and a large effect size when*r*= 0.5 or more. - The significance of the relationship: If
*r*, as demonstrated by the*p*-value, is significantly different from zero then we can assume the identified relationship between the variables in our sample did not arise by chance (i.e., sampling error) and we have found a relationship of significance.

The benefit of

*r*is that it can also tell us how much variance both variables share, i.e. how much of the variance in one variable is present in the second variable. This is achieved by calculating the coefficient of determination (R²), which is simply done by squaring

*r*(Dancey & Reidy, 2014).

**Checks & assumptions**

As the Pearson’s correlation coefficient is a parametric test certain assumptions and checks must be made to ensure the data is fit for purpose. In total it is wise to undertake these seven checks before the test is run (Steele et al., 2012; Field, 2014):

- Data must be at the interval or ratio levels, or if one variable is interval or ratio then the other is dichotomous.
- Normally distributed data is required (i.e., not positively or negatively skewed).
- A linear relationship exists between the data (i.e., check via a scatter plot).
- Homoscedasticity is required with equal variance between variables (i.e., variances along the line of best fit are equally similar). This would be violated (i.e., heteroscedasticity) if for example, variances at one end of the scatter plot were of equal magnitude but at the opposite end the variances were widely dispersed, resulting in a funnel shape (Figure 6).
- Data is provided for each variable, both x and y.
- Independence of observation (i.e., participant scores are not influenced by other participants’ scores).
- Outliers are identified for potential distortion of the output, particularly in smaller sample sizes when one or two outlying scores can more easily impact the value of
*r*. For example, a low correlation could be enhanced by some high outliers (Figure 7), whereas a high correlation could be limited by some low outliers (Figure 8) (Forshaw, 2007; Laerd Statistics, 2013).

**How to interpret the SPSS output**

When it comes to interpreting the output of a Pearson’s correlation coefficient in SPSS there are several steps to be taken (Forshaw, 2007; Steele et al., 2012):

(1) The most important first step is to do a visual check of the scatterplot for signs that a correlation exists. If no apparent correlation is present, then it is likely there is no correlation. (2) Assuming you do observe a correlation pattern, then proceed to check the

*r*value for the strength and direction of the relationship. (3) Once a correlation has been confirmed it is then necessary to ascertain whether it is in fact significant (

*p*<0.05), or whether the correlation has arisen by chance (

*p*>0.05). (4) At this point, if the level of significance is close to

*p*=0.05 it is wise to check the impact of any outliers and how the result changes, if at all, when these are included or excluded.

**Be aware**

We can only correlate scores if they come from the same population, meaning we cannot compare relationships between different populations. Moreover, in a correlation we simply measure and observe relationships, we do not try to influence them or control them as we do in experiments, therefore we cannot state cause and effect from a correlation. It is also important to be aware of the ‘Third Variable Problem’, where changes in x and y may be due to the impact of an extraneous variable, which may or may not be under consideration (Field, 2014).

**Worked example: The Daily Mile**

Much has been made of the obesity rate in children and the need to improve children’s activity and fitness levels (BBC, 2015). A primary school in Dundee is interested in rolling out a scheme known as the ‘Daily Mile’ programme, where children run or walk a mile each day at school (The Scottish Government, 2015). Before the head teacher considers investing in the programme she is interested in finding out if a relationship exists between the weight of the children (variable w) and the time they take to complete a mile (variable t). She is aware the average weight for an 11 year old based on data from the WHO (Disabled World, 2016) is 36.25 kg (i.e., 35.6kg for girls, 36.9kg for boys), and the ‘healthy fitness zone’ pass rate to complete one mile at this age is 11 minutes 30 seconds (i.e. 12.00 mins for girls, 11.00 mins for boys) (Running School, 2007). One lunchtime she invites 40 pupils aged 11 years old (20 boys & 20 girls) to complete a mile and collects data about their weight and finish times (Table 1).

Pupil | Gender | Weight (kg) | Time to finish 1 mile (mins:secs) | Pupil | Gender | Weight (kg) | Time to finish 1 mile (mins:secs) |
---|---|---|---|---|---|---|---|

1 | Boy | 35.5 | 11.45 | 21 | Girl | 33.7 | 11.35 |

2 | Boy | 35.9 | 11.45 | 22 | Girl | 34.6 | 13.15 |

3 | Boy | 36.2 | 12.17 | 23 | Girl | 34.9 | 10.59 |

4 | Boy | 36.2 | 10.39 | 24 | Girl | 35.2 | 12.15 |

5 | Boy | 36.4 | 12.55 | 25 | Girl | 35.2 | 10.15 |

6 | Boy | 36.4 | 12.11 | 26 | Girl | 35.8 | 10.32 |

7 | Boy | 36.9 | 10.31 | 27 | Girl | 36.2 | 12.03 |

8 | Boy | 37.2 | 12.49 | 28 | Girl | 36.3 | 13.31 |

9 | Boy | 37.3 | 12.55 | 29 | Girl | 36.9 | 10.31 |

10 | Boy | 37.8 | 11.42 | 30 | Girl | 36.9 | 11.01 |

11 | Boy | 38.6 | 12.19 | 31 | Girl | 37.3 | 11.59 |

12 | Boy | 38.9 | 14.11 | 32 | Girl | 37.4 | 14.12 |

13 | Boy | 39.3 | 12.33 | 33 | Girl | 37.8 | 11.45 |

14 | Boy | 39.4 | 13.29 | 34 | Girl | 38.2 | 12.28 |

15 | Boy | 40.5 | 14.03 | 35 | Girl | 38.3 | 13.05 |

16 | Boy | 40.6 | 11.45 | 36 | Girl | 39.1 | 13.04 |

17 | Boy | 40.8 | 14.15 | 37 | Girl | 39.4 | 13.15 |

18 | Boy | 41.1 | 13.59 | 38 | Girl | 39.8 | 13.42 |

19 | Boy | 41.2 | 14.42 | 39 | Girl | 40.1 | 12.45 |

20 | Boy | 14.02 | 40 | Girl | 38.7 | 13.23 |

**Assumptions & checks**

The type of data collected is ratio data, for both the children’s weight and finish times, and was normally distributed, which means it can be used to run the Pearson’s correlation coefficient. By examining the scatterplot (Figure 9) we can see a positive linear relationship exists and data has been collected on both variables for all forty participants. Homoscedasticity is also present, with no disparity of variance along a potential line of best fit. Moreover, the head teacher was able to confirm that the assumption of independence of observation holds true. However, had it been the case that some children had agreed to run the mile together and finish together it would have violated this assumption. Also, there appear to be no outliers which might distort the

*r*value.

Moreover, as illustrated by Figure 10, there also appears to be a positive association between gender and finish times, with girls (coded as 1) taking longer to complete the mile than boys (coded as 0), which is in line with the estimates from the Running School (2007).

**Interpretation**

When Pearson’s correlation coefficient test was run in SPSS a significant, large, positive correlation existed between the children's weight and the time taken to complete one mile,

*r*= .510, n=40,

*p*<0.01. Higher weight levels in children were associated with longer times to complete the mile (Table 2). A significant, large, positive correlation was also found for the effect of gender on finish times,

*r*= .520, n=40,

*p*<0.01, with girls taking longer to complete the mile. However, a significant, medium, negative relationship was found between gender and weight, with girls weighing less than boys,

*r*= -.373, n=40,

*p*<0.05.

That said, the amount of variance shared by weight and finish times when gender was controlled for is .887, and significant (

*p*<0.000). Therefore, the value of R² for the partial correlation is .79, meaning weight shares 79% of the variance in finish times, compared to 26% (.51 x .51) when gender was not controlled for. Whilst this association does not imply causality it does help to underscore the association between weight and fitness, which is the focus of efforts through programs such as the Daily Mile to reduce obesity.

By D. Anderson (University of Dundee, Academic Year 2015-16)

**What is it?**

The

*t*-test is a parametric statistical test of normal distribution (to be more precisely a

*t*-distribution) which allows us to compare two sample means of a continuous dependent variable, identify the amount by which they differ and determine whether this difference is statistically significant. The standardised statistical output is a

*t*-value. Higher

*t*-values are associated with lower

*p*-values, which indicates the distribution of results are of statistical significance rather than a product of sampling error, when the null hypothesis is true (Field, 2014).

**Why use a**

*t*-test?The

*t*-test is particularly useful in experiments where we want to assess the difference between two experimental conditions and there is one outcome variable (Keep in mind that there is also a One-Sample t-test where one sample mean is compared to a critical value to test whether it significantly differs from it or not.). In particular, its flexibility and sensitivity allows it to accommodate the nuances of experimental design when different participants (i.e., between-subjects design) partake in the two experimental conditions, or when the same participants are used in each condition (i.e., within-subject design). This means we can tailor the test to our selected experimental design (Steele, et al., 2012; Dancey & Reidy 2014):

- Independent
*t*-test (Independent Measures): use when the two sets of mean scores come from two separate groups of participants. - Paired Samples (Dependent Measures)
*t*-test: use when the two sets of mean scores come from the same participants.

**How it works**

The

*t*-test is premised on a model which seeks to detect differences in the two sample group means in such a way as to apportion out systematic variance explained by the model (i.e., the experimental effect) and unsystematic variance which cannot be explained by the model (i.e., sampling error) (Field, 2014).

There are several important points to be mindful of when working with the top and bottom sections of the equation (Field, 2014):

- If the experiment has no effect and the null hypothesis is true we would expect to see very little difference between the two sample means. Small differences might arise naturally, but large differences in means would be unexpected within one sample.
- Since no two samples are identical it is important to calculate the standard error which might have arisen between the two sample means in order that we can detect if the differences have arisen by chance or whether in fact we have a statistically meaningful result.

**The importance of the standard error**

In brief, the standard error is the standard deviation of the sample means drawn from the population. The standard error plays an important role within the

*t*-test. Importantly, by dividing the average differences of means by the standard error this turns the t-test into a valuable tool of comparison, irrespective of the unit of measurement contained within the dependent variable. Moreover, it allows us to see the anticipated representativeness of the sample mean differences against the population. Large standard errors indicate a wide distribution of sample means, meaning there is a greater likelihood of large differences between samples. Whereas small sample errors indicate closely distributed means round the centred zero, meaning large differences would be unlikely (Field, 2014). In essence, when a small standard error is accompanied by a large difference between the sample means then we have a situation where the experimental manipulation has taken effect rather than an effect which has arisen by chance.

**Be aware**

Due to the potential increase in Type I error the

*t*-test cannot be used if there are more than two groups for comparison, when for example the ANOVA test would be recommended. Moreover, as a parametric test, the assumptions of data at the interval/ratio level, normality of distribution, random sampling, assumption of independence and homogeneity of variance must be met (Steele, et al., 2014).

**Taking the test with the daily mile**

It is now eight weeks on from the initial assessment of the children’s times to complete the mile. During this time the children have completed the daily mile each day at school. The same children who took part in the initial trial mile at week zero have been tested again at the end of week eight. The head teacher now wishes to find out if there has been an overall improvement in finish times (irrespective of gender) and whether this has been significant or not.

A paired-samples

*t*-test will allow us to compare the overall mean finish times between the two time trials. As highlighted by the SPSS output (Table 5), there has been a significant improvement in finish times from week zero to week eight,

*t*(39) = 5.85,

*p*=.000. Moreover, the standard error of the mean is small at 0.08, which indicates that the viability between different possible samples of 11-year-olds at the school would be small. By D. Anderson (University of Dundee, Academic Year 2015-16)

**ANOVA**A one-way ANOVA is used where a researcher wants to compare the means of more than two conditions; typically, three or more mean scores are compared. ANOVA can inform as to whether there are significant differences between the means of the groups you want to compare. For example, a researcher may be interested in investigating non-pharmacological treatment for post-traumatic stress disorder and outcome (e.g., whether symptoms decrease). There may be three levels of the independent variable (treatment), for example, (1) cognitive behavioural therapy, (2) eye movement desensitization and reprocessing therapy and (3) group therapy. Symptom reduction is the only dependent variable. The one-way ANOVA would look to compare the treatment groups to see if there were any significant differences in mean scores between them. There are, however, other variables that may be correlated with the outcome variable of symptom reduction; for example, whether individuals with depression are receiving pharmacological interventions such as antidepressants, or the level of social support the individual has. These ‘additional’ variables can be entered as covariates. An ANCOVA (analysis of covariance) or MANCOVA (multivariate analysis of covariance) will include and statistically control for the influence these covariates may be having on the dependent variable. An ANCOVA is therefore an extension of the one-way ANOVA, in that it includes covariates. A MANCOVA is an extension of an ANCOVA because it encompasses more than one dependent variable, as well as also including covariates.

There are two main reasons why is it is useful to include covariates in an ANOVA analysis:

- In an ANOVA analysis, it is possible to explain the effect that the experiment has had by comparing variability: the amount that can be attributed to the experiment and the amount that is unaccounted for by the experiment and is therefore ‘unexplained’ variance. Covariates may be able to explain some of this unexplained variance when they are included in the covariance analysis and thus explain some of the error variance. Consequently, the effect of the independent variables is clearer.
- Including and controlling for covariates in the analysis quantifies their influence on the dependent variable.

**Assumptions of an ANCOVA**

Before an ANCOVA can be conducted, there are several assumptions that the data need to meet:

- Both the covariate and dependent variable must be continuous variables (interval or ratio level)
- The independent variable should consist of two or more levels (e.g. gender: male and female)
- Each participant must be in a different experimental group and participants should not be in more than one group (otherwise known as independence of observations)
- Outliers in the data must not exist
- The dependent variable should be normally distributed, e.g., assessed by Shapiro-Wilk test of normality.
- There must be homogeneity of variances (the assumption that all groups have identical or similar variances). This can be assessed using Levene’s test.
- The covariate should be linearly related to your dependent variable at each level of the independent variable in order to reduce error variance and so increase ANCOVA sensitivity.
- Homoscedasticity must exist (where the error variance is equivalent across all values of the independent variables).
- There must not be any interaction between the covariate and independent variable (otherwise known as homogeneity of regression slopes).

**Case study**

A researcher wanted to investigate peak expiratory flow, measured in litres per minute (l/min), in seven non-smoking men aged 50 at three different times: firstly after smoking a cigarette, secondly after exercising for 30 minutes and thirdly in normal circumstances (control condition).

Participant | Time 1: After smoking | Time 2: After exercise | Time 3: Control condition | Time 1-Time 2 | Time 1–Time 3 | Time 2–Time 3 |
---|---|---|---|---|---|---|

1 | 575 | 570 | 590 | 5 | -15 | -20 |

2 | 590 | 600 | 610 | -10 | -20 | -10 |

3 | 590 | 600 | 600 | -10 | -20 | 0 |

4 | 585 | 585 | 590 | 0 | -5 | -5 |

5 | 570 | 575 | 588 | -5 | -18 | -13 |

6 | 585 | 590 | 600 | -5 | -15 | -10 |

7 | 575 | 575 | 580 | 0 | -5 | -5 |

Variance: | -25 | -98 | -63 |

By Kathryn Wilson (University of Dundee, Academic Year 2015-16)

### Dare to check your knowledge?

Take The Big Knowledge Test by D. Anderson

(University of Dundee, Academic Year 2015-16)