Our bachelor research is about distance perception in the peripersonal space. The experiment was already complex to design with the oculus rift, but the statistical analysis appears impossible :shakehead.
We are trying to fit the data we gathered till now into the right analysis but we are having a hard time
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The experiment set up:
35 participants; stimuli seen through Oculus Rift (3D): approaching cones that disappeared at different distances (conditions: near (20 cm), mid (60 cm) and far (120 cm). Participants estimated the distance where the cones disappeared in the virtual 3D environment by moving a square backwards. The perceived threat for the three conditions was measured on a 7point-Likert scale (but it appears to be a 8point scale, since some judged it to be 0)
. Moreover, participants received a motor vibration cue on the left or right cheek depending on the heading direction of the cone. The instruction was to press a button as soon as possible after a motor vibration cue was perceived. Cue onset timing was either at a random interval (random cue condition), or depended on the time it took the cone from the distance it dissapeared to blow in the participants face (fixed cue condition). We also have STAI questionnaire scores for Trait and State anxiety levels (max score of 80 each). All variables were measured multiple times for all individuals that participated in all conditions.
So we have 1 IV (stimulus distance) with three levels (near, mid, far) and 3 DV’s: Distance Estimation, Threat and Response time (with 2 additional levels: fixed/random) all measured multiple times for each individual in all three distance conditions near, mid far.
The dataset contains a score (average) for every individual and includes:
1. Estimation in cm (3 levels): Distance_Estimation_Near, Distance_Estimation_Mid, Distance_Estimation_Far.
2. Perceived threat 0-7 scale (3 levels): Threat_Near, Threat_Mid, Threat_Far
3. Response speed in ms (2x3 levels): RS_Near_Fixed, RS_Mid_Fixed, RS_Far_Fixed and also RS_Near_Random, RS_Mid_Random and RS_Far_Random.
+ State and Trait anxiety score.
Our research goal is to examine whether cones/threat in the peripersonal space (near) would be more threatening and more underestimated in distance than outside the space more distant from the observers. Also if there are more estimation errors with high threat in the conditions. We want to find out if underestimation might be adaptive for a fast reaction, so we want to find out if response speed is also faster/slower for the distance estimation errors at different distance conditions (near/mid/far). We also included state and trait anxiety to find out of this had any influence on the estimation, threat and response speed. High state anxiety was thought to increase the distance underestimation, threat and a faster response speed in the peripersonal space condition.
I ran individual repeated measures ANOVA’s on distance estimation(3), threat(3) and response speed(3x2) in SPSS. However, we also need to find a way to include interactions into an analysis (such as threat on distance estimation, and possible modulation of distance estimation on response speed via threat or state/trait anxiety levels) to test or hypotheses.
Been through all three statistic's study books, but the appropriate analysis for this sort of design seems no-where to be found.
We are trying to fit the data we gathered till now into the right analysis but we are having a hard time
The experiment set up:
35 participants; stimuli seen through Oculus Rift (3D): approaching cones that disappeared at different distances (conditions: near (20 cm), mid (60 cm) and far (120 cm). Participants estimated the distance where the cones disappeared in the virtual 3D environment by moving a square backwards. The perceived threat for the three conditions was measured on a 7point-Likert scale (but it appears to be a 8point scale, since some judged it to be 0)
So we have 1 IV (stimulus distance) with three levels (near, mid, far) and 3 DV’s: Distance Estimation, Threat and Response time (with 2 additional levels: fixed/random) all measured multiple times for each individual in all three distance conditions near, mid far.
The dataset contains a score (average) for every individual and includes:
1. Estimation in cm (3 levels): Distance_Estimation_Near, Distance_Estimation_Mid, Distance_Estimation_Far.
2. Perceived threat 0-7 scale (3 levels): Threat_Near, Threat_Mid, Threat_Far
3. Response speed in ms (2x3 levels): RS_Near_Fixed, RS_Mid_Fixed, RS_Far_Fixed and also RS_Near_Random, RS_Mid_Random and RS_Far_Random.
+ State and Trait anxiety score.
Our research goal is to examine whether cones/threat in the peripersonal space (near) would be more threatening and more underestimated in distance than outside the space more distant from the observers. Also if there are more estimation errors with high threat in the conditions. We want to find out if underestimation might be adaptive for a fast reaction, so we want to find out if response speed is also faster/slower for the distance estimation errors at different distance conditions (near/mid/far). We also included state and trait anxiety to find out of this had any influence on the estimation, threat and response speed. High state anxiety was thought to increase the distance underestimation, threat and a faster response speed in the peripersonal space condition.
I ran individual repeated measures ANOVA’s on distance estimation(3), threat(3) and response speed(3x2) in SPSS. However, we also need to find a way to include interactions into an analysis (such as threat on distance estimation, and possible modulation of distance estimation on response speed via threat or state/trait anxiety levels) to test or hypotheses.
Been through all three statistic's study books, but the appropriate analysis for this sort of design seems no-where to be found.
What analysis is possible or the most appropriate to test the hypotheses for this dataset?
Any advice is appreciated!
Any advice is appreciated!