Skip to main content

Posts

Showing posts with the label Data modeling

Fibonacci Life Chart and Psychology

  The Fibonacci sequence is a series of numbers where each number is the sum of the two previous numbers. The sequence starts with 0 and 1, and then continues with 1, 2, 3, 5, 8, 13, 21, and so on. The number of leaves on a plant or the number of branches on a tree will often follow the Fibonacci sequence. Robert G. Sacco (2013) developed the Fibonacci Life Chart Method (FLCM) and applied it to Erikson’s eight developmental stages (1982). Sacco presented a revised age-stage chart. Following is a quote from Sacco’s discussion (p. 143). The results of this study provide support for the assumption of an eight-stage theory of development. The FLCM serves several useful functions. These include: (a) substantially improving understanding of the eight developmental life stages proposed by Erikson, and (b) the use of it as a tool for timing of interventions.     References Erikson, E. H. (1982). The life cycle completed . New York: Norton. Sacco, R. G. (2013). Re-envisaging the eig

Watch those Covid 19 curves

Some highly intelligent scientists have used available data to plot trends related to the spread of Covid-19 such as number of identified infections, recoveries, and deaths. Others plotted ideas rather than data such as flattening the curve. Some rely on "common sense" and others rely on data. Given my experience in teaching graduate research methods and statistics and talking with physicians and psychologists about statistics, I think it worth reminding any readers of this post that a lot of very intelligent people have difficulty with statistical models. There are many PhDs younger than I who know more methods of sophisticated data analyses than I learned during my PhD or since; however, not everyone stops to consider their assumptions when modeling data. That's my point here--we need to review and challenge the assumptions about the data, the way the data are plotted, and how people interpret those data. Missing data are very important to developing an accurat