It is a variable which the researcher can manipulate. It is a treatment or program or presumed cause.

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The following examples are nominal variables except:

_______variables involves data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless.

If you are distributing a survey, pilot-test it first to ensure that the instructions, questions and scale items are clear.

 The ___________ of a scale indicates how free it is from random error.

The ________ of a scale refers to the degree to which it measures what it   is supposed to measure.

It is a type of validity that refers to the adequacy with which a measure or scale has sampled from the  domain of content.

To define each of the variables that make up your data file, you first need to click on the _______________ at the bottom left of your screen. In this view the variables are listed down the side, with their characteristics listed along the top (name, type, width, decimals, label etc.).

In defining variables in SPSS a researcher will open the variable view. In this column, she will type in the brief variable name that will be used to identify each of the variables in the data file (listed in your codebook.

The _____ column allows you to provide a longer description for your variable than used in the Name column.

The column heading _______ refers to the level of measurement of each of your variables.

It describes the way in which data are ‘spread'.

It describes the likelihood of something happening based on what we know about previous outcomes.

This is the most common number in a data set.

________ describes the ‘peakedness’ of the curve.

The outcome is ‘statistically significant’ if there is a less than 5% probability that it happened by chance or (more precisely) that there is a less than 5% probability that the null hypothesis is true.

In one-tailed hypothesis, a specific, directional prediction, e.g. we might predict that patients' anxiety scores will improve after undergoing cognitive therapy.

If we find that women do report higher mood scores than men and statistical analyses indicate that there is a less than 5% probability that this happened by chance, we can reject the null hypothesis.

It is demonstrated by the extent that scores vary around the mean score.

It provides an indication of the symmetry of the distribution.

One of the most commonly used indicators of internal consistency is Cronbach’s alpha coefficient.

Factor analysis allows you to condense a large set of variables or scale items down to a smaller, more manageable number of dimensions or factors.

Analysis of covariance (ANCOVA) is used when you want to statistically control for the possible effects of an additional confounding variable (covariate).

It is an analysis that is used to describe the strength and direction of the linear relationship between two variables.

It is designed for interval level (continuous) variables. It can also be used if you have one continuous variable (e.g. scores on a measure of self-esteem) and one dichotomous variable (e.g. sex: M/F).

It is designed for use with ordinal level or ranked data.

The scatterplot can tell you whether the relationship between your two variables is positive or negative. An upward trend indicates a positive relationship; high scores on X associated with high scores on Y.

It is a family of techniques that can be used to explore the relationship between one continuous dependent variable and a number of independent variables or predictors.

Checking of normality in regression analysis means that the residuals should be normally distributed about the predicted DV scores.

Checking of linearity in regression analysis means that the residuals should have a straight-line relationship with predicted DV scores.

In regression analysis, homoscedasticity refers to the variance of the residuals about predicted DV scores that should be the same for all predicted scores.

Independent-samples t-test is used when you want to compare the mean scores of two different groups of people or conditions.

Paired-samples t-test is used when you want to compare the mean scores for the same group of people on two different occasions, or when you have matched pairs.

If the significance level of Levene’s test is p=.05 or less (e.g. .01, .001), this means that the variances for the two groups (males/females) are not the same.

An independent-samples t-test is used when you want to compare the mean score, on some continuous variable, for two different groups of participants.

Between-groups ANOVA is used when you have different participants or cases in each of your groups (this is referred to as an independent groups design)

Repeated measures analysis of variance is used when you are measuring the same participants under different conditions (or measured at different points in time) (this is also referred to as a within-subjects design).

One-way between-groups ANOVA is used when you have one independent variable with three or more levels (groups) and one dependent continuous variable.

One-way ANOVA will tell you whether there are significant differences in the mean scores on the dependent variable across the three groups.