A factor analysis of the SSQ (Speech, Spatial, and Qualities of Hearing Scale)

OBJECTIVE: The speech, spatial, and qualities of hearing questionnaire (SSQ) is a self-report test of auditory disability. The 49 items ask how well a listener would do in many complex listening situations illustrative of real life. The scores on the items are often combined into the three main sections or into 10 pragmatic subscales. We report here a factor analysis of the SSQ that we conducted to further investigate its statistical properties and to determine its structure. DESIGN: Statistical factor analysis of questionnaire data, using parallel analysis to determine the number of factors to retain, oblique rotation of factors, and a bootstrap method to estimate the confidence intervals. STUDY SAMPLE: 1220 people who have attended MRC IHR over the last decade. RESULTS: We found three clear factors, essentially corresponding to the three main sections of the SSQ. They are termed "speech understanding", "spatial perception", and "clarity, separation, and identification". Thirty-five of the SSQ questions were included in the three factors. There was partial evidence for a fourth factor, "effort and concentration", representing two more questions. CONCLUSIONS: These results aid in the interpretation and application of the SSQ and indicate potential methods for generating average scores

Dealing With Spatial Heterogeneity in Entrepreneurship Research

In quantitative research, analyses are generally made using a geographically defined population as the study area. In this context, the relationships between predictor and response variables can differ within the study area, a feature that is known as spatial heterogeneity. Without analyzing spatial heterogeneity, a global model may not be correct, and there may be unclear spatial boundaries in the generalizability of the findings. The authors discuss how the method of geographically weighted regression (GWR) can be used to identify the study area, and illustrate the utility of GWR for empirical analyses in entrepreneurship research. Future entrepreneurship research can benefit from analyzing whether conflicting evidence may be due to spatial heterogeneity and from applying GWR in an exploratory way