Entering Data for Factorial Designs When collecting data from an experiment with a factorial design (i.e. an experiment with more than one independent variable), you need to make sure that you enter your data into SPSS correctly… otherwise you will not be able to carry out a meaningful analysis. The way you enter data for your variables varies according to whether they are between-participants, within-participants or a combination of the two. This tutorial will walk you through the different ways of entering data for the different factorial ANOVAs: Independent, Repeated-Measures and Mixed. Whether you are manually entering experimental data yourself, or if you are using experimental software (such as OpenSesame or E-Prime) to collect data, you need to make sure your data file adheres to one of these formats before you run your analyses. Worked Example To illustrate how to enter data appropriately into SPSS, let’s consider a fictional factorial study that could be investigated in three different ways. Imagine you are a researcher who is concerned about the effects of alcohol on people’s ability to react to hazards while driving. You know that high levels of alcohol result in slower reaction times, so you want to investigate the effects of lower (legal) levels of alcohol consumption. You also believe that hazard detection ability might be affected by lighting conditions whilst driving. To investigate this, you could ask participants to drink different liquids containing either no alcohol, or enough alcohol to raise blood levels to 0.025% BAC, or 0.05% BAC. After drinking the liquid, participants could take part in a driving simulation test in which several hazards occur (e.g. a pedestrian stepping out in front of you). The simulations occur in both day and night lighting conditions. Participants have to react to the hazards a quickly as possible, and their reaction time is measured. In this example, we have a factorial design which has:

two independent variables: o alcohol level (3 levels: none, low and moderate) o lighting conditions (2 levels: day and night)

one dependent variable: average reaction time to hazards As one IV has 3 conditions, and the other has 2 we can call this a ‘3 x 2 design’. This means that there are 6 (3x2 = 6) possible condition combinations.

The number of these condition combinations participants take part in depends entirely on the way you design the study. For example, do participants:

take part in only one condition for each IV (i.e. between-participants)?

take part in all conditions of all IVs (i.e. within-participants)?

or a mixture of the two (i.e. a mixed design)? Between-Participants Design First, let’s look at what our data would look like if we ran this study with a between-participants (or ‘independent’) design. In a completely between-participants design, both IVs are between-participants. This means that participants only drink one level of alcohol, and only drive in one lighting condition. This means that they will only provide reaction time data for one of the six possible condition combinations. For example, this table represents a participant who took part in the night time task under low alcohol conditions:

Research Tip: the order in which participants are allocated to between-participants conditions should be randomised.

As a general rule in SPSS, one row should contain all of the data provided by one participant. In a between-participants design, this means that we have one column for our DV and separate columns for each of the IVs. In the IV columns, individual participants are given a code which represents the condition that they belong to.

This is how data would need to be entered into SPSS for analysis:

The columns are:

Part_ID : This refers to the ID number assigned to the participants. We use numbers as identifiers instead of participant names, as this allows us to keep track of the data we collect (i.e. identify which participants took part in which conditions) while keeping the participants anonymous.

Alcohol : This column represents our first independent variable. Codes have been used to tell SPSS which condition participants had been allocated to (i.e. how much alcohol each of the participants consumed). In this case:

1 = none 2 = low 3 = moderate

Lighting : This column represents our second independent variable. This column tells us the time of day the driving simulation took place. Codes have been used to tell SPSS which condition each of the participants belonged to. In this case:

1 = day time 2 = night time

RT : This column displays how quickly (on average) participants successfully responded to hazards whist in the driving simulator. This is our dependent variable and it is measured in milliseconds.

If you need to remind yourself which numerical codes represent which conditions, you can ask SPSS to display this for you by selecting the A < - > 1 button. CLICKING this button displays the code names.

To explore how the variables were entered into SPSS, you can view the variable details in Variable View.

In Variable View you can see more detail about how the variables in this example were added. Specifically, you can see how condition values were assigned to the between-participants IVs, and how the DV scores were defined. To find out more about how to enter variables, revisit the tutorial Adding Variables on the statistics resource.

Mixed Design An alternative way to run this study would be as a mixed design. In this case one of our IVs could be kept as between-participants (alcohol consumption), while the other could be run within-participants (time of day). In this study, participants either drink no alcohol, enough to raise their BAC to 0.025% or enough to raise them to 0.05% BAC. Then they would all take part in the two driving simulation tasks, both in day and night time lighting conditions. Again, there are six possible condition combinations... but in a mixed design, participants each take part in two of these conditions. For example, this table represents a participant who was allocated to the high alcohol condition: Research Tip Remember, when dealing with repeated-measures variables, it is important to counterbalance the order in which the different conditions are presented (in order to minimise bias). In this case, half of the participants would take part in the day hazard detection test followed by the night condition, while the other half of participants would do the opposite. And again, the order in which participants were allocated to between-participants conditions would need to be randomised. As always in SPSS, each row in the spreadsheet should contain all of the data provided by one participant. Within-participants variables use separate columns to represent each of the conditions/levels (as each participant contributes multiple data points). Between-participants variables are coded in a separate column, where the different levels or conditions of the IV refer to separate individuals.

This is how data would need to be entered into SPSS for analysis:

The columns in the dataset are:

Part_ID: Again, this refers to the ID number assigned to the participants. We use numbers as identifiers instead of participant names, as this allows us to keep track of the data we collect (i.e. identify which participants took part in which conditions) while keeping the participants anonymous.

Alcohol: This column represents our between-participants IV. Again, codes are used to tell SPSS which condition participants had been allocated to (i.e. how much alcohol each of the participants consumed). In this case:

1 = none 2 = low 3 = moderate

Day_RT: This column represents one level of the within-participants IV: Lighting. It contains participants’ average reaction time to hazards when driving in the simulated day time condition.

Night_RT: This column represents the second level of the within-participants IV: Lighting. It contains participants’ average reaction time to hazards when driving in the simulated night time condition.

You can find our more about how the variables were entered into SPSS by viewing the variable details in Variable View.

In Variable View you can see more detail about how the variables in this example were added. Again, you can see how condition values were assigned to the between-participants IV. However, this time you can also see how the different levels of the within-participants IV were entered into the different columns. To find out more about how to enter variables, revisit the tutorial Adding Variables on the statistics resource.

Repeated-Measures Design The final option for running this study would be as an entirely within-participants (or repeated-measures) design, where all participants take part in all experimental conditions. In this study, all participants would need to be tested on three different occasions where they either drink a liquid containing no alcohol, enough alcohol to raise their BAC to 0.025% or enough to raise them to 0.05%BAC. After each drink, they would then take part in the two driving simulation tasks, both in day and night time lighting conditions. In this case it means that participants provide 6 data points for analysis, one for each of the condition combinations:

Again, counterbalancing the order of the different conditions is necessary in this type of design to minimise bias. This is what the data should look like in SPSS:

Again, remembering that one row contains all of the data for each participant, in an entirely within-participants design we need one column for each possible experimental condition combination. In a 3x2 design, as there are 6 possible condition combinations, we need to make sure there are at least 6 columns. As there are no between-participants variables, no coding columns are necessary. The columns in the dataset are:

Part_ID: As with the other designs, this refers to the ID number assigned to the participants. We use numbers as identifiers instead of participant names, as this allows us to keep track of the data we collect (i.e. identify which participants took part in which conditions) while keeping the participants anonymous.

No_Day_RT: This column represents one of the six possible condition combinations. In this case is contains the mean reaction time to hazards when the simulation was in the day time, and participants had drunk no alcohol.

Low_Day_RT: This column represents one of the six possible condition combinations. In this case is contains the mean reaction time to hazards when the simulation was in the day time, and participants had a blood alcohol level of 0.025% BAC.

Mod_Day_RT: This column represents one of the six possible condition combinations. In this case is contains the mean reaction time to hazards when the simulation was in the day time, and participants had a blood alcohol level of 0.05% BAC.

No_Night_RT: This column represents one of the six possible condition combinations. In this case is contains the mean reaction time to hazards when the simulation was in the night time, and participants had drunk no alcohol.

Low_Night_RT: This column represents one of the six possible condition combinations. In this case is contains the mean reaction time to hazards when the simulation was in the night time, and participants had a blood alcohol level of 0.025% BAC.

Mod_Night_RT: This column represents one of the six possible condition combinations. In this case is contains the mean reaction time to hazards when the simulation was in the night time, and participants had a blood alcohol level of 0.05% BAC.

Again, you can find out more about how the variables were entered into SPSS by viewing the variable details in Variable View.

In Variable View you can see more detail about how the variables in this example were added.

Here you can see that a column has been created for each of the possible condition combinations so that participants’ reaction times under each condition can be directly entered into SPSS. As there are no between-participants IVs, there are no columns containing condition codes. You have now seen how you need to format your data if you are going to analyse experimental data collected using a factorial design. Whether you are collecting data manually or using experimental software (like OpenSesame or E-Prime), you need to make sure that you think carefully about:

what format you need to get your data into before you run your ANOVA

the best way of getting your data into the correct format (for example, if your experimental conditions comprise multiple trials - like in our hazard detection study - you may need to calculate average scores to represent your DV)

Remember - your data needs to be entered correctly for your SPSS analysis to be valid and reliable.