A human-computer interaction (HCI) researcher was interested in examining whether humans are better able to use a
Question:
A human-computer interaction (HCI) researcher was interested in examining whether humans are better able to use a joystick or a mouse to point a computer cursor. She therefore constructed an experiment in which participants used either a joystick (joystick condition) or a mouse (mouse condition) to point a cursor to a target displayed on a computer monitor. She measured the time (seconds) that it took to place the cursor over the target (dependent variable referred to as time, where a higher time score indicates poorer performance). To determine whether potential benefits of using the joystick or mouse generalized to difficult HCI scenarios, the target was either stationary (static condition) or moved slowly across the computer screen at a constant velocity (motion condition). Participants were randomly and uniquely assigned to 1 of 4 conditions in the interface (levels: joystick, mouse) × target type (levels: static, motion) experimental design. Given the data collected by the researcher (see Table 1 below), what can she conclude about how easily humans interact these HCI interfaces and how are these HCI interfaces influenced by target type? Include a line graph of the means.
For the analysis, assume a two-tailed hypothesis test with α = .05 unless otherwise specified. All parametric analyses must include effect sizes. Any t-tests must include 95% confidence intervals. Any analysis that includes an F-test must also include an accompanying ANOVA table. Any analysis that includes a correlation must also include the variances of the variables and their cross product (SP; covariability). Any analysis that includes linear regression must also include all relevant descriptive statistics (i.e., SSResidual, standard error of the residual, regression equation). Any non-parametric analysis must include the ranked scores and the sum of ranks (ΣR).