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Point measure correlation6/10/2023 ![]() ( 2015) named the two dimensions (a) Job-search self-efficacy behaviour (JSSE-B) and (b) Job-search self-efficacy outcomes (JSSE-O). The JSSE scale is a 20-item measure, composed of two dimensions that tap important aspects of the job search beliefs and outcomes. ![]() ( 2015) organised the various measures and integrated them into a single measure, the Job-Search Self-Efficacy (JSSE) scale. To address the proliferation of job search self-efficacy measures in the job search literature in order to offer opportunities for the comparison of research results across studies, Saks et al. Despite this, the measurement of job search self-efficacy has been somewhat diverse with various researchers developing and using their own measure to assess the construct (see Caplan, Vinokur, Price, & van Ryn, 1989 Ellis & Taylor, 1983 Wanberg, Zhang, & Diehn, 2010 van Hoye, Van Hooft, Stremersch, & Lievens, 2019). Job search self-efficacy is defined as the confidence in one’s ability to successfully search for jobs and to gain employment, and has long been found to be the most proximal determinant of employment among job seekers (Eden & Aviram, 1993 Kanfer & Hulin, 1985 Kanfer, Wanberg, & Kantrowitz, 2001 Saks & Ashforth, 1999). doi:10.Self-efficacy theory (Bandura, 1997) has provided a crucial framework for understanding job search beliefs and effort for 30-odd years (see Kim, Kim, & Lee, 2019 Saks, Zikic, & Koen, 2015). “Determining the Significance of Associations between Two Series of Discrete Events : Bootstrap Methods”. Freeman (2007), On the association between northward turnings of the interplanetary magnetic field and substorm onsets, Geophys. (1992), Time association between series of geophysical events, Phys. (1955), Some statistical methods connected with series of events, J. (1976), Measuring the association of point processes: A case history, Am. Performing this bootstrapping procedure K times allows us to model the sampling variation in n(u, h).Ī further treatment of assessing confidence intervals using bootstrapping techniques is given by Niehof and Morley, but the above should work for two series of neuronal spike trains (or similar simple system). Repeating this for every lag u, we construct a bootstrap estimate of the association number with lag n*(u, h). Summing these N randomly-sampled associations gives a bootstrap estimate of the association number for given u, h. Using this set of individual associations, we can construct a new series, c*$_i$, by drawing with replacement a random selection of N individual associations. we see that n(u, h) is a summation of the N individual associations c$_i$ for given u, h. The approach taken by Morley and Freeman was to instead resample from the individual association numbers. When taking this approach it's important to understand the system as resampling the series $A$ and $B$ may not work without applying, say, a moving block bootstrap to preserve correlations in the spike trains. If neither series is Poisson then a bootstrapping approach can be used to estimate confidence intervals. are therefore indicative of a significant association between the event sequences at certain lags. Where $p_A$ and $p_B$ are the mean intensities of series $A$ and $B$, given by $p_A = N/T$, where $N$ is the number of events in $A$ (similar for $B$). If those assumptions are met then 95% confidence intervals on the cross-product density can be estimated by Correlations at different time lags can then be seen by inspecting the cross-product density for departures from 1 (if the processes are independent the cross-product density should be 1, which is expected at large lags for most physical processes).Īssessing the significance of these departures can be addressed in a number of different ways, but many assume that at least one of the processes is Poisson. If we normalize by $2hT$, where $T$ is the length of the interval from which our samples were drawn, then we get an estimate of the cross-product density. If series $A$ and $B$ are uncorrelated then the association number will fluctuate, as a function of lag, due to sampling variations, but will have a stable mean. This is generally calculated for a range of time lags, $u$, such that we get $n(u,h=const)$ ]. The individual association number, $c$, is then the number of events in series $B$ that fall within a given window, and $n$ is then defined as To calculate $n$ a window of half-width $h$ is defined around each time in series $A$. The simplest measure of association between two temporal point processes (let's call them $A$ and $B$) is probably the association number, $n$. One of the early, seminal works on point processes is that of Cox. A good introduction to measuring correlations between point processes, as applied to neuronal spike trains, is given by Brillinger. A series of event times is a type of point process.
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