SPSS Experimental Exam with
Complete Solutions
ANOVA - An acronym for the ANalysis Of VAriance. By analysing the variance in the
data due to different sources (e.g. an independent variable or error) we can decide if our
experimental manipulation is influencing the scores in the data.
between subjects - Also known as independent measures. In this design, the samples
we select for each condition of the independent variable are independent, as a member
of one sample is not a member of another sample.
between-subjects design - a different group of subjects is tested under each condition
comparisons - The results of a statistical test with more than two conditions will often
show a significant result but not where the difference lies. We need to undertake a
comparison of conditions to see which ones are causing the effect. If we compare them
two at a time this is known as pairwise comparison and if we perform unplanned
comparisons after discovering the significant finding these are referred to as post hoc
comparisons.
condition - A researcher chooses levels or categories of the independent variable(s) to
observe the effect on the dependent variable(s). These are referred to as conditions,
levels, treatments or groups. For example, 'morning' and 'afternoon' might be chosen as
the conditions for the independent variable of time of day.
confidence interval - In statistics we use samples to estimate population values, such as
the mean or the difference in means. The confidence interval provides a range of values
within which we predict lies the population value (to a certain level of confidence). The
95 per cent confidence interval of the mean worked out from a sample indicates that the
population mean would fall between the upper and lower limits 95 per cent of the time.
contrasts - With a number of conditions in a study we may plan a set of com - parisons
such as contrasting each condition with a control condition. These planned comparisons
are referred to as contrasts. We can plan complex contrasts - for example, the effects of
conditions 1 and 2 against condition 3.
correlation - The degree to which the scores on two (or more) variables co-relate. That
is, the extent to which a variation in the scores on one variable results in a
corresponding variation in the scores on a second variable. Usually the relation - ship
we are looking for is linear. A multiple correlation examines the relationship between a
combination of predictor variables with a dependent variable.
, critical value - We reject the null hypothesis after a statistical test if the probability of the
calculated value of the test statistic (under the null hypothesis) is lower than the
significance level (e.g. .05). Computer programs print out the probability of the
calculated value (e.g. .023765) and we can examine this to see if it is higher or lower
than the significance level. Textbooks print tables of the critical values of the test
statistic, which are the values of the statistic at a particular probability. For example, if
the calculated value of a statistic (i.e. a t test) is 4.20 and the critical value is 2.31 (at the
.05 level of significance), then clearly the probability of the test statistic is less than .05.
degrees of freedom - When calculating a statistic we use information from the data
(such as the mean or total) in the calculation. The degrees of freedom is the number of
scores we need to know before we can work out the rest using the information we
already have. It is the number of scores that are free to vary in the analysis.
dependent variable - The variable measured by the researcher and predicted to be
influenced by (that is, depend on) the independent variable.
descriptive statistics - Usually we wish to describe our data before conducting further
analysis or comparisons. Descriptive statistics such as the mean and standard deviation
enable us to summarise a dataset.
discriminant function - A discriminant function is one derived from a set of independent
(or predictor) variables that can be used to discriminate between the conditions of a
dependent variable.
distribution - The range of possible scores on a variable and their frequency of
occurrence. In statistical terms we refer to a distribution as a 'probability density
function'. We use the mathematical formulae for known distributions to work out the
probability of finding a score as high as or as low as a particular score.
effect size - The size of the difference between the means of two populations, in terms
of standard deviation units.
equality of variance - See homogeneity of variance .
F statistic - A statistical test to determine the relationship between the variances of two
or more samples.
factor - Another name for 'variable', used commonly in the analysis of variance to refer
to an independent variable. In factor analysis we analyse the variation in the data to see
if it can be explained by fewer factors (i.e. 'new' variables) than the original number of
independent variables.
grouping variable - In analyzing data in SPSS we can employ an independent measure
independent variable as a grouping variable. This separates our participants into groups
Complete Solutions
ANOVA - An acronym for the ANalysis Of VAriance. By analysing the variance in the
data due to different sources (e.g. an independent variable or error) we can decide if our
experimental manipulation is influencing the scores in the data.
between subjects - Also known as independent measures. In this design, the samples
we select for each condition of the independent variable are independent, as a member
of one sample is not a member of another sample.
between-subjects design - a different group of subjects is tested under each condition
comparisons - The results of a statistical test with more than two conditions will often
show a significant result but not where the difference lies. We need to undertake a
comparison of conditions to see which ones are causing the effect. If we compare them
two at a time this is known as pairwise comparison and if we perform unplanned
comparisons after discovering the significant finding these are referred to as post hoc
comparisons.
condition - A researcher chooses levels or categories of the independent variable(s) to
observe the effect on the dependent variable(s). These are referred to as conditions,
levels, treatments or groups. For example, 'morning' and 'afternoon' might be chosen as
the conditions for the independent variable of time of day.
confidence interval - In statistics we use samples to estimate population values, such as
the mean or the difference in means. The confidence interval provides a range of values
within which we predict lies the population value (to a certain level of confidence). The
95 per cent confidence interval of the mean worked out from a sample indicates that the
population mean would fall between the upper and lower limits 95 per cent of the time.
contrasts - With a number of conditions in a study we may plan a set of com - parisons
such as contrasting each condition with a control condition. These planned comparisons
are referred to as contrasts. We can plan complex contrasts - for example, the effects of
conditions 1 and 2 against condition 3.
correlation - The degree to which the scores on two (or more) variables co-relate. That
is, the extent to which a variation in the scores on one variable results in a
corresponding variation in the scores on a second variable. Usually the relation - ship
we are looking for is linear. A multiple correlation examines the relationship between a
combination of predictor variables with a dependent variable.
, critical value - We reject the null hypothesis after a statistical test if the probability of the
calculated value of the test statistic (under the null hypothesis) is lower than the
significance level (e.g. .05). Computer programs print out the probability of the
calculated value (e.g. .023765) and we can examine this to see if it is higher or lower
than the significance level. Textbooks print tables of the critical values of the test
statistic, which are the values of the statistic at a particular probability. For example, if
the calculated value of a statistic (i.e. a t test) is 4.20 and the critical value is 2.31 (at the
.05 level of significance), then clearly the probability of the test statistic is less than .05.
degrees of freedom - When calculating a statistic we use information from the data
(such as the mean or total) in the calculation. The degrees of freedom is the number of
scores we need to know before we can work out the rest using the information we
already have. It is the number of scores that are free to vary in the analysis.
dependent variable - The variable measured by the researcher and predicted to be
influenced by (that is, depend on) the independent variable.
descriptive statistics - Usually we wish to describe our data before conducting further
analysis or comparisons. Descriptive statistics such as the mean and standard deviation
enable us to summarise a dataset.
discriminant function - A discriminant function is one derived from a set of independent
(or predictor) variables that can be used to discriminate between the conditions of a
dependent variable.
distribution - The range of possible scores on a variable and their frequency of
occurrence. In statistical terms we refer to a distribution as a 'probability density
function'. We use the mathematical formulae for known distributions to work out the
probability of finding a score as high as or as low as a particular score.
effect size - The size of the difference between the means of two populations, in terms
of standard deviation units.
equality of variance - See homogeneity of variance .
F statistic - A statistical test to determine the relationship between the variances of two
or more samples.
factor - Another name for 'variable', used commonly in the analysis of variance to refer
to an independent variable. In factor analysis we analyse the variation in the data to see
if it can be explained by fewer factors (i.e. 'new' variables) than the original number of
independent variables.
grouping variable - In analyzing data in SPSS we can employ an independent measure
independent variable as a grouping variable. This separates our participants into groups
